Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

1

NASA ARMD WILDFIRE MANAGEMENT WORKSHOP

Workshop – 13 May 2021

Authors:

Dr. Parimal Kopardekar

Laurie Grindle

Additional Contributors:

Dr. Richard Mogford 

Richard Walsh 

Christine Clark 

Anna Cavolowsky 

Jonathan Nguyen
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Table of Contents 

NASA ARMD WILDFIRE MANAGEMENT WORKSHOP..............................................................................................3

EXECUTIVE SUMMARY.....................................................................................................................................................3

BACKGROUND...............................................................................................................................................................4

WILDFIRE MANAGEMENT ASSESSMENT ACTIVITY OVERVIEW .................................................................................................5

WILDFIRE MANAGEMENT ASSESSMENT REQUEST FOR INFORMATION ......................................................................................6

WILDFIRE MANAGEMENT WORKSHOP OVERVIEW ...............................................................................................................7

General Session ....................................................................................................................................................7

Breakout Sessions.................................................................................................................................................9

BREAKOUT SESSIONS’ DISCUSSION NOTES ..........................................................................................................................9

Planning for Fire Season.......................................................................................................................................9

Prediction Modeling and Challenges..................................................................................................................11

Aerial Fire Surveillance .......................................................................................................................................15

Suppression and Mitigation ...............................................................................................................................16

Post-fire Remedial Efforts ..................................................................................................................................18

POSSIBLE NEXT STEPS ...................................................................................................................................................20

Comprehensive Concept of Operations, Systems Engineering Practice, and Transition to Reality ....................20

Leverage NASA Efforts and Programs................................................................................................................21

Data and Modeling ............................................................................................................................................21

Persistent Surveillance .......................................................................................................................................22

Organizational Considerations...........................................................................................................................22

Wildfire Management Aircraft...........................................................................................................................22

Persistent Communication .................................................................................................................................23

Cooperative Airspace Management for Manned and Unmanned Operations ..................................................23

Wildfire Suppression Duration ...........................................................................................................................23

Supporting Firefighting Personnel......................................................................................................................24

SUMMARY..................................................................................................................................................................24

APPENDIX A | NASA ARMD WILDFIRE MANAGEMENT WORKSHOP AGENDA........................................................................25

APPENDIX B | BREAKOUT GROUPS’ QUESTIONS ................................................................................................................26

APPENDIX C | BREAKOUT GROUPS’ TOP 3 OUTCOMES .......................................................................................................31

APPENDIX D |POSSIBLE NEXT STEPS HIGH-LEVEL DESCRIPTIONS...........................................................................................41

APPENDIX E | BREAKOUT GROUPS’ TOP 3 OUTCOMES AND POSSIBLE NEXT STEPS CROSSWALK..................................................43

APPENDIX F | BREAKOUT GROUPS’ TOP 3 OUTCOMES AND POSSIBLE NEXT STEPS CROSSWALK PLOTS.........................................58
Dr. Parimal Kopardekar; Laurie Grindle

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NASA ARMD WILDFIRE MANAGEMENT WORKSHOP

Workshop – 13 May 2021

Executive Summary

In response to the increasing severity and cost of wildfires in the United States, the NASA 

Aeronautics Research Mission Directorate (ARMD) conducted a one-day Wildfire Management 

Workshop on May 13, 2021. This document represents a summary of the discussions of 

workshop participants. The NASA Aeronautics Research Institute (NARI) at the NASA Ames 

Research Center organized the event. The goal of the workshop was to provide a forum for 

representatives from wildfire organizations (government, private, and academic) to share 

insights into community needs, challenges, and solutions, and help to inform NASA's 

contributions to the wildfire community. The proceedings consisted of presentations from the 

U.S. Forest Service, NASA Space Technology Mission Directorate, and NASA Applied Sciences 

Disasters Program. Also included were two sessions of breakout groups that focused on specific 

wildfire management issues. 

Major themes that emerged from the breakout groups included data access and management, 

wildfire modeling, improving organizational collaboration, the use of aircraft in fighting fires 

and collecting data, and providing front line firefighters with the information tools they need to 

be effective. The output of the workshop is being shared with the broader community for 

consideration of national challenges and opportunities and used by NASA to consider avenues 

where the agency might contribute expertise and technology to assist with wildfire 

management.

The possible next steps were derived from multiple facets of the Wildfire Management 

Assessment Activity, including the workshop discussions, Subject Matter Expert (SME) inputs, 

literature review, and Request for Information (RFI) responses. They are summarized below and 

in Appendix D and organized in alignment with NASA aeronautics capabilities for further 

consideration by NASA and the broader wildfire community.

Comprehensive Concept of Operations, Systems Engineering Practice, and Transition to 

Reality: Develop multi-agency unified concept of operations to align priorities, response 

strategies, information exchanges and integration, technologies and their interoperability 

requirements, and coordinated wildfire management approach to maximize the impact. Use 

systems engineering approach to identify multiple types of users’ needs, map the users’ needs 

into functional requirements, and identify technology and procedure related considerations. 

This could result in a product roadmap for multiple users in the wildfire management 

community. Develop an approach to transition research and concepts into reality. The users 

need mature high technology readiness level products. This will require coordination and 

collaboration among multiple government agencies, industry, and non-profit organizations.
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Leverage NASA Efforts and Programs: Collaborate with NASA STMD for wildfire focused 

competitions, prizes, and SBIR/STTR topics. Partner with NASA SMD to leverage data assets and 

tools that are beneficial to wildfire management

Data and Modeling: Conduct comprehensive data and models inventory analysis, identify gaps 

and overlaps, and identify integration and interoperability considerations for multiple types of 

users. This will maximize value of data and information to the respective users when they need 

it most.

Persistent Surveillance: Improve the surveillance of the fire regions by using a combination of 

sensor data from satellite and High-Altitude Long Endurance (HALE) flights.

Organizational Considerations: Improve coordination between federal, state, county, 

municipal, and private organizations involved in wildfire prevention and management, and 

break down barriers that impede information flow to produce significant benefits.

Wildfire Management Aircraft: Assess the needed aircraft fleet, their capabilities, and 

associated technologies that will be most effective in wildfire management. Relevant use cases 

include surveillance, suppression, and remedial inspections.

Persistent Communication: Develop concepts and technologies that will provide reliable, 

persistent communications (voice and data) in remote, hazardous areas.

Cooperative Airspace Management for Manned and Unmanned Operations: Develop 

roadmap for automated (e.g., UTM-in-a-box) airspace management technology. The roadmap 

could include common situational awareness capability as an early step and fully automated 

self-contained airspace operations as the end goal.

Wildfire Suppression Duration: Increase the suppression duration in day and night (24/7) 

through automated aircraft, automated airspace operations, and safety assurance methods to 

ensure persistent and safe operations.

Support Firefighting Personnel: Provide the timely, accurate knowledge needed by the 

firefighters through improvements in gathering data, running models, and collaborating 

between agencies.

Background

In the United States, wildfires are becoming increasingly severe and costly in terms of acreage 

burned, property damage, and most importantly, lives lost. Wildfire frequency and intensity is 

escalating, inducing budgetary, personnel, and equipment challenges. Furthermore, California 
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and other western states have been facing persistent drought conditions and much hotter 

temperatures, which are fueling wildfire intensity and duration. These alarming trends have 

made it urgent to recognize how wildfires could be better predicted, mitigated, and managed 

to reduce their impact.

NASA’s Science Mission Directorate (SMD) and Space Technology Mission Directorate (STMD) 

have a history of contributions to wildfire management including remote sensing, 

instrumentation, mapping, data fusion, and prediction. Additionally, NASA’s aeronautics 

community has possible contributions to manage suppression and mitigation efforts through 

decision support tools for airspace operations coordination that would reduce human workload 

during firefighting operations.

The NASA ARMD is seeking to gain insights from the stakeholder community to identify 

technology needs to support the management of wildfires, map NASA ARMD technology 

investments and capabilities to community requirements, and determine areas where ARMD, in 

collaboration with others internal and external to NASA, can contribute to better planning, 

prediction, mitigation, and remediation of wildfires and their impact. To accomplish this, a 

Wildfire Management Assessment Request for Information (RFI) was initiated and NASA hosted 

a Wildfire Management Workshop on May 13, 2021. It is important to note that, although we 

will share high-level input received from the RFI, this document will primarily focus on the 

feedback received during the workshop. 

NASA ARMD, in collaboration with SMD, the U.S. Air Force and U.S. Forest Service, conducted 

the workshop on Wildfire Management. The United States Forest Service and NASA SMD, 

working through the Tactical Fire Remote Sensing Advisory Committee (TFRSAC),1 were 

instrumental in the development, planning and orchestration of the event. The workshop was

comprised of a General Session and two Breakout Sessions. The former established the setting, 

objectives, and goals of the event as well as emphasizing the importance NASA places on 

wildfire management in the broader context of disaster management and recovery, viewing 

both as national priorities. The latter provided relevant information that will be used to inform 

decisions regarding ARMD’s potential contributions to wildfire solutions. 

This document provides an overview of the workshop, including key findings, takeaways, and 

gaps to improve the efficiency and effectiveness of the state of the art of wildfire management.

Wildfire Management Assessment Activity Overview

Mr. Robert Pearce, the Associate Administrator of NASA ARMD, created the Wildfire 

Management Assessment Activity to listen to the wildfire management community, understand 

their needs, and determine how NASA ARMD can best contribute to addressing those needs. 

The activity’s general approach includes standing up a NASA Wildfire Management Assessment 

1 https://fsapps.nwcg.gov/nirops/pages/tfrsac
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Team; conducting an internal ARMD research and technology inventory assessment; engaging 

in discussions with the wildfire community; developing plans and potential near-, mid-, and farterm activities for consideration; and identifying potential cost sharing partners. The RFI and 

workshop are integral to this approach. 

Wildfire Management Assessment Request for Information

NASA invited the wildfire management community to respond to an RFI. The purpose of the RFI 

was to aid NASA in gaining insights from the stakeholder community regarding potentially 

relevant technologies and capabilities. RFI responses were reviewed, revealing seven primary 

themes with corresponding challenges and solutions proposed by respondents. These themes

and challenges are detailed below. 

One key challenge is ensuring sufficient funding for wildfire management. Currently, most of 

the funding is spent on fire suppression costs. A proposed solution would be to increase or refocus a portion of the funds on research (including attracting more students from relative fields 

of study), technological development, service testing, and deployment/implementation.

Another challenge is related to data management and availability as it pertains to procurement

of new technology and equipment. There needs to be a way to swiftly license, customize, 

deploy, and train at scale. A couple of recommendations were to create a “steering board” or to 

establish a process for transitioning Small Business Innovation Research (SBIR) Phase III 

technologies to the applicable government agencies. 

Collective access to higher resolution and improved real-time wildfire-related data is 

instrumental in improving tactical utility and supporting cross-organizational coordination. This 

information includes satellite remote sensing data (e.g., accurate fuels mapping) and data that 

is otherwise only available during public disasters or located on separate systems (e.g., 

lightning, neighboring incidents, and transitioning aircraft). 

Furthermore, data sources are scattered across agencies and programs creating training and 

operational inefficiencies. The wildfire disaster management community needs seamlessly 

integrated equipment to view and understand relevant data, which does not require extensive 

training for scaled, cross-jurisdictional use and, in turn, enables collaborative decision making.

Solutions proposed to address operational efficiencies included an intuitive, streamlined

computing infrastructure, applicable products delivered to the field, quick reaction forward 

bases for vertical takeoff and landing (VTOL) aircraft, non-pilot operator training, a testing 

environment to simulate fire conditions, mission specific unmanned aircraft systems (UAS)

(e.g., for monitoring/detection, firefighting (liquid payload), etc.), dimensional products to 

visualize airspace structures, and more. 

Another challenge is to develop relevant and consistent standards and regulations for wildfire 

management. Advances in technologies and capabilities have outpaced the scope of current 
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policy. A couple of examples are UAS cyber security within civilian and science-based

organizations and airspace management. Overall, there is a need for common language and 

tools for preplanning, training, establishing an operating picture, alerts and notifications, and 

community engagement. 

As we move more into the digital age, there is concern regarding the security and reliability of 

the Internet infrastructure that supports the field equipment. Many wildfires are in remote 

locations where Internet connectivity may not be available or able to handle the amount of 

data and applications required to manage the fire. 

Lastly, buy in from the wildfire management community is key in order to assess and adopt new 

capabilities. New systems and technologies must be user friendly, reliable, and dependable in 

the circumstances in which they are intended to be used in order to ensure their acceptance. 

Several federal, state, and local entities are involved in firefighting, and through continued 

collaboration, effectiveness could be increased. New concepts and technologies should enable 

such collaboration. 

It is important to note that this section is a summary of inputs provided by RFI respondents and

is not considered prioritized or exhaustive. Additionally, the RFI is still open. To complete the 

RFI, visit: https://nari.arc.nasa.gov/wildfiremgmt. 

Wildfire Management Workshop Overview 

Dovetailing off the Tactical Fire Remote Sensing Advisory Committee’s (TFRSAC) Spring 2021 BiAnnual Meeting, NASA assembled a day-long workshop, including a general session and two 

breakout sessions. To view the complete event webpage, visit: https://nari.arc.nasa.gov/tfrsacwildfire. (Please reference Appendix A for the event agenda.)

The purpose of the workshop was to facilitate NASA’s understanding of wildfire management, 

challenges, and obstacles. Participants, including government organizations involved in forest 

and land management (e.g., U.S. Air Force, U.S. Forestry Service, U.S. Geological Survey, 

National Guard, CAL FIRE, etc.), the firefighting community, industry, and academia, worked to 

identify challenges and gaps where help is needed and discussed how NASA’s capabilities in

research, development, testing, and implementation could improve the state of the art. In the 

following sections, this paper will document the deliberations and key outcomes from the 

workshop.

General Session

The General Session established the setting, objectives, and goals of the workshop, as well as 

hosted keynote speakers and a fireside chat.

To start the event, Robert Pearce, Associate Administrator of NASA ARMD, Richard Barhydt, 

Station Director for the Pacific Southwest Research Station of U.S. Forest Service, and Nathan 
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Diller, Director of AFWERX, provided opening remarks. Mr. Pearce spoke about the goals of

understanding community needs, better identifying challenges, and helping to inform NASA's 

contributions to the wildfire community. There may be aspects of the wildland fire community 

mission that could be enhanced through aviation. 

Richard Barhydt shared his excitement about new partnerships and how the workshop could be 

a steppingstone to collaborate on a much larger scale. This opportunity brings new ideas and 

solutions. Col. Diller talked about the AFWERX approach to the changing technology space and 

how new tools can address some of these growing threats from wildfires. AFWERX is looking 

forward to supporting NASA and the U.S. Forest Service in pushing the limits of key 

technologies that can better manage wildfires. 

Robert Baird, Regional Director of Fire and Aviation Management in the Pacific Southwest 

Region of the U.S Forest Service, was the keynote speaker for the workshop. He provided an 

operational overview and talked about the critical requirements of the Wildland Fire Program 

and services in California. Overall, the community needs a common operating picture across all 

devices. Other needs include near-real-time infrared (IR) wildfire detection and tracking;

projection in highly dynamic, remote, austere environments; capabilities for nighttime

operations; air attacks (e.g., having a person in the air to observe the important areas for

suppression); early detection and synthesis across platforms; and game-changing air tankers

(e.g., VTOL, autonomous, large-capacity, etc.).

After the first breakout session, Jenn Gustetic, Director of Early Stage Innovations and 

Partnerships for NASA’s STMD, shared the variety of opportunities for industry, as well as other 

entities, to collaborate in terms of prizes and competitions, Small Business Innovation Research 

(SBIR), and Small Business Technology Transfer (STTR). STMD Early Stage Innovations and 

Partnerships Portfolio makes available approximately $300 million in annual funding for these 

and other programs. 

David Green, Program Manager for NASA’s Earth Science Applied Sciences Disasters Program, 

presented on research and development, focusing on the Mapping Portal.2 The portal is 

informed by global observations and analyses that are used to support real-world events 

throughout the disaster cycle. NASA looks at the full cycle, including planning, prediction,

mitigation, suppression, and integration with airborne aerial and supporting satellite 

components. The challenge is processing the information on a scale that will help make 

decisions for early action. The Disasters Program is looking forward to collaborating with NASA 

Aeronautics to make even greater contributions in this area. 

After the second breakout session, Parimal Kopardekar hosted a fireside chat with Vincent 

Ambrosia and Everett Hinkley, the co-leads of the TFRSAC. The TFRSAC is a longstanding 

“Community of Practice” co-hosted by the U.S. Forest Service and NASA SMD, which focuses on 

2 https://data.nasa.gov/Earth-Science/NASA-Disasters-Mapping-Portal/vyzu-gm3d
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sharing best practices and creating knowledge to advance the wildland management domain. In 

the fireside chat, Vince and Everett shared their views about how the TFRSAC has matured over 

the last 18 years.

To conclude the general session, Jon Montgomery, Deputy Associate Administrator for Policy of 

NASA ARMD, provided closing remarks. He spoke about how technology has matured in the last 

20 years and that near-term operationalization or integration of unmanned systems into the 

national airspace is possible. In addition, there have been great advances in artificial 

intelligence (AI) and machine learning, data management and gathering, and data fusion. There 

is a lot more information at our fingertips today and the time has come for NASA Aeronautics to 

help improve the state of the art of wildfire management. 

Breakout Sessions

There were two, one-hour breakout sessions organized around five themes. The five themes 

were Planning for Fire Season, Prediction Modeling and Challenges, Aerial Fire Surveillance, 

Suppression and Mitigation, and Post-Fire Remedial Efforts. Each breakout session was 

comprised of 10 separate, moderated group discussions with a total of 54 questions asked. 

Each group had a cross-section of participants (e.g., government, industry, and academia) to 

promote education and inclusivity. (Please reference Appendix B for the full list of breakout 

questions and Appendix C for the top three outcomes from each breakout group.)

Breakout Sessions’ Discussion Notes 

This section is a summary of workshop participant contributions from the breakout discussions, 

organized by the five themes. This includes a recounting of the various views shared during 

breakout sessions. The top three outcomes from each breakout session are listed in Appendix 

C. It is important to note that this section is not prioritized or exhaustive, nor does it represent 

a consensus of participant views. In some places, the summary highlights possible alignment 

with NASA capabilities and participant recommendations for NASA ARMD activities, as well as 

recommendations for the broader wildfire community.

Planning for Fire Season

The four Planning for Fire Season breakout groups focused on acting before fires occur. This 

included strategies to reduce the frequency, severity, and size of wildfires and the associated 

losses and costs. Examples are fuel breaks to stop wildfires, wildfire protection zones to protect 

communities, maintaining healthy forests, prescribed burns, predicting fire risks, and 

community involvement. It is also important to assess and identify public and private resources 

that could be damaged by wildfires. There is a need to develop and mobilize around improved 

plans for fire management that include considerations of economic and financial impact. The 

Planning for Fire Season breakout groups included discussion on computational fire modeling. 

This material has been moved to the Prediction Modeling and Challenges section.
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To accomplish this planning, wildfire management agencies need current, reliable, 

comprehensive, and regularly updated data sets that include information about cities, counties, 

and state governments. Also essential is information on commercial oil and gas pipelines, 

electric transmission lines, building footprints, roads, structures, water and power 

infrastructure, fire weather, fuel moisture, vegetation, etc. Data is currently captured in paper 

form (e.g., Facilitated Learning Analysis, Incident Management Reports) but is moving to be 

increasingly digital. Proposed solutions include increasing digital data capture, overcoming

barriers to sharing restricted digital data, and utilizing this for simulations and incident planning 

and management. There is also a requirement for timely and actionable real-time information 

(e.g., wind and weather predictions and tracking and prediction of fires).

The participants in the Planning for Fire Season breakout groups also noted there could be

Department of Defense (DoD) and NASA capabilities to understand how to assess situations and 

develop tactics and strategies. They suggested that these agencies could develop partnerships 

with the National Wildfire Coordinating Group (NWCG).

3 The NWCG is the umbrella 

organization that sets training and certification standards. NASA could connect with NWCG to 

open channels of communication. This could help with collaboration on physics-based 

modeling, advanced computing methods, and data collection and distribution techniques.

Sustained collaboration with the research community outside of fire season would help 

improve the quality and availability of data.

Lessons learned are largely retrospective. Opportunities exist and could be expanded to be 

more anticipatory and pro-active, translating lessons into predictions while recognizing that 

that fires are now year around. Lessons learned are being captured by the Wildfire Lesson 

Center4 over different time scales. However, the data is heterogeneous, and this hampers 

analysis. It might be possible to leverage other similar real-time processes and data solutions 

for disasters such as volcanoes, earthquakes, floods, etc.

Solutions are needed for funding, procurement, data management, operationalization of data 

analyses, and communications (including distribution of data and information). There is also a 

need for firefighters to participate in research and development (as future users) for the 

practical maturation of technology for real world use. The fire community is small and widely 

distributed. More community exchanges are needed to work out possible solutions. Perhaps 

there could be another focused workshop on partnerships and problem solving that primarily 

includes front line operators. It was suggested that NASA could act as a facilitator of 

communications and collaboration.

3 https://www.nwcg.gov/

4 https://www.wildfirelessons.net/home
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There is an opportunity for better integration of DoD. DoD has a tremendous amount of 

technology and experience in regards to knowing where people are, strategy, intelligence, etc.

DoD is recognizing this and working on a cohesive wildland fire strategy, which is still in its 

infancy stages. DoD research is looking at prescribed fire (planned burns) as well as research 

called FASMEE.5

The National Guard is a state asset that might be leveraged to support coordination between 

DoD, NASA, the national fire agencies, U.S. Forest Service, and Department of the Interior.

Participants suggested NASA could serve as a conduit for agreements. CAL FIRE has a good 

model for how to interact with state and federal agencies to establish rapid agreements, 

training, and understanding to support wildfire activities. This or a similar model could be used 

to improve coordination and collaboration. 

The firefighting community needs the state of the art from 2010 to be an operational capability 

in 2021. "The state of the art is useless when you're all alone digging a fire ditch, unless it's in 

your pocket or in your pack." Operators could talk with short term technology infusers (one to 

two-year solutions) to work out how commercial-off-the-shelve tools can be applied today.

Community building and coalitions are integral in synchronizing state governments and private 

bodies. Once the fire can be observed, small UAS (sUAS) methods could be applied. Innovative 

technologies are out there but are not operational. Participants suggested NASA could fund 

companies to push development and move new tools to an operational state. It is important to 

apply technologies that are now available. An organization is needed that can work with the 

private sector and implement these changes, creating a process to apply these technologies 

and to evolve the community.

Prediction Modeling and Challenges

The four Prediction Modeling and Challenges breakout groups discussed data acquisition, 

transmission, and application, and computational modeling of wildfires. Information and 

predictions are critical for wildfire management, but there are issues with managing and using 

large data sets and complex models.

Fire services use models to predict fire impacts (e.g., effects on roads, structures, and 

vegetation) for fire treatment planning, tree mortality, fire danger operating plans, and 

developing preplanned fire scenarios for expected response. These models could be integrated 

into the Wildland Fire Decision Support System, which assists fire managers and analysts in 

5 The Fire and Smoke Model Evaluation Experiment (FASMEE) is a multi-agency, interdisciplinary collaborative 

effort to identify and collect critical measurements of fuels, fire behavior, fire energy, meteorology, smoke, and 

fire effects that will be used to evaluate and advance operational-used fire and smoke models. 

https://www.fasmee.net/
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making strategic and tactical decisions for fire incidents. New modeling techniques could be 

used to predict fire spread. AI could be trained with small controlled fires.

Fire services also use computer models to anticipate various factors that affect wildfires. The 

Rothermel fire-spread model was developed by Richard Rothermel and first published in 1972. 

It “reduces a forest fire to a set of equations operating in a hypothetical universe in which fires 

burn only small, uniform, dead fuels on the forest floor. The model does not need to know what 

species of trees or shrubs are growing on a site, except as they are represented as “fuel” of one 

type or another. The model attempts to describe mathematically the physical and chemical 

processes of fire.” However, the Rothermel model needs to be updated. 

Other models for predicting fire behavior include BehavePlus, FARSITE, FlamMap, FireFamily 

Plus, Rear Event Risk Assessment Process, WindWizard, FireStem, and the Wildland Fire 

Assessment System. NASA has developed the Global Fire Weather Database6that provides 

wind, temperature, and humidity data to forecast where fires could begin. There are currently 

no models to predict impact to water and power infrastructures. There might be people 

available on innovation teams at insurance and energy companies that could help with this.

A participant noted that the Los Alamos National Lab is doing great work with a fluid dynamics 

program called Firetec. Next Generation physics-based fire behavior models have the potential 

to be game changers, but they are very data intensive. For instance, they require 3D fuel 

information, e.g., light detection and ranging (LiDAR) data. Efforts are underway on how to 

directly input LiIDAR and moisture data.

The many different proprietary data sources can be problematic. Lack of standards creates 

problems when using data updates for models. The data can be for different time scales, 

densities, and rates. Information on fuels and localized weather are especially important for 

modeling and prediction. Predictive computer models should have access to the best data

available. More coordination is needed between modelers, data producers and data providers. 

The community at large probably has a good idea of what standard data formats should be 

used. This may need a push from one of the government agencies to help define and structure

those standards. Standards, such as OpenGIS, may be adequate. It would be very helpful if a 

university or similar entity would conduct a periodic comprehensive survey of data sources and 

modeling efforts and publish the results. The information could be posted publicly for reference

by the modeling community.

6 https://data.giss.nasa.gov/impacts/gfwed/
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The current procedure in fire behavior modeling is spatially limited (i.e., localized). There is a 

great potential to improve the predictive capabilities of fire behavior models using tools like 

computational fluid dynamics. It might be possible to predict where fire could happen before it 

starts. However, the computational power required will be high, likely requiring the use of 

supercomputers. 

Researchers want to be able to validate their technology in real world conditions, but 

firefighters are rightfully concerned about testing low maturity technologies in life-threatening 

conditions. A framework could be developed that allows for better interaction between 

operators, technology developers, and researchers.

It is important to identify situations where there can be tests before fire situations develop.

Prescribed burns could provide a laboratory for evaluations of new models. The wildfire world

is a small technological community.

Participants noted that NASA Aeronautics has some interesting technology initiatives that might 

be relevant to address wildfire related challenges, such as those related to aircraft and airspace 

operations, data management, safety technologies, flight tests, etc. 

In August 2020, there were 600 fires at the same time, making model building very difficult.

Many models take six hours to run which limits their use for real time decision making. Real, 

actionable data is needed on what is going on with the population in the area of the fire. This 

year CAL FIRE started GPS tracking of their units, but there is limited information on the 

location of the public.

A California system has been up and running since last season. The web site has an integration 

bar with incidents. The tracking of resources comes from the audio calls and 911. It runs an 

automatic simulation that shows on a scale of 1 to 5 how complicated the fire is. A new system 

called JEDI displays wind conditions and impacts on buildings or population. It delivers the 

information with a one-page report that the firefighters have when they are dispatched and go 

into the field. It includes tracking of resources, communications, and provides situation 

awareness.

Some of the data sets from NASA (such as the Geostationary Operational Environmental

Satellite Program (GOES))

7 have not been fully integrated with available tools. There is also 

interest in higher resolution (temporal as well as spatial) data for prediction modeling. Soil 

moisture for topography is one specific data set identified for possible inclusion in data models.

High resolution atmospheric “vertical data” provides information on weather, particularly at a 

7 https://www.nasa.gov/content/goes
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small scale, with different altitudes and effects from topography. The stratospheric 

infrastructure could be sampled by balloons and other airborne platforms.

Small drones with sensing capabilities could track rapidly moving fires and provide high 

resolution imagery when needed. The fire policy considerations may be a limiting factor. It is 

difficult for non-federal fire fighters to get the training, approval, and aircraft for that mission.

Drones from China sometimes cannot be used. There are bigger drones, operated by private 

contractors, assigned into the large fires. They perform like the multi-mission aircraft and can 

provide overwatch on areas that are rapidly burning, but they struggle at mapping the fire.

Because they have a constricted view (i.e., soda straw effect), they have to slowly fly around 

the entire perimeter at lower elevations than manned aircraft.

It is important to deliver appropriate information to the right people at the right time.

Communication is very difficult in remote areas and in extreme conditions. There is an 

opportunity to take ground truth data collected during a fire and make it more available to 

people developing models. All participants carry cell phones. The fire engines have larger radios 

that are more powerful. It is also important to get the information to the people on the ground.

For the Camp Fire, casualties resulted when the wind shifted, and they were caught unaware.

Cell phones and radios can be linked via Bluetooth to augment capabilities and provide access 

to greater amounts of data. The goal is a 15-minute data update. Anything outside 15 to 30 

minutes is more than the operational loop time. There needs to be a better way for scientists 

and engineers to interact with the user community and to have time in actual incidents to 

understand requirements.

There are wide area sensors (not whisk8 or IR scanners) that are able to capture a broad view of 

the fire and identify the perimeters, but they are not on aircraft yet. These sensors could deliver 

a frequent, high-resolution picture of the fire and provide a low-resolution image of where the 

fire is strongest at any given time.

Data is needed at a higher spatial and temporal resolution (100 meters with a 15 to 30-minute 

update) to be relevant for fast moving fires. Data should be integrated and shared across 

sources and users. Geo-location accuracy of fires is important (+/- 50 meters). There is no need 

to update an entire map if data size or transmission time is a concern. Instead focus on an area 

of the map to update to reduce size or transmission requirements.

8 https://svs.gsfc.nasa.gov/12754
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

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Aerial Fire Surveillance

Two breakout groups focused on aerial fire surveillance. The main themes of discussion 

included improved aircraft and sensors, advantages of autonomous vehicles, and a safe 

airspace environment for these operations.

In these discussions, it was suggested that it is time to replace aging aircraft with specifically 

designed aircraft that consider sensors, payload, distribution processes, and efficiency of drops. 

Previously mentioned issues regarding data transmission and machine learning to amalgamate 

and create usable information were noted. Participants stated that not enough funding is 

directed at data collection, fusion, integration, and delivery. So even with better aerial 

surveillance equipment, real time information flow to the field is challenging without policies, 

procedures, and workflows in place.

Specific sensor equipment was not discussed, but when it comes to data collection, altitude is 

more important than range. A combination of ground cameras (especially on peaks), aircraft, 

and satellites would be needed to provide data from all altitudes. For detection, a GOES 

satellite has a 2-km pixel capability and can see a 1-hectare brush fire on a typical day.

However, fires tend to be 15 – 25 minutes old when noted. 

Unlike satellites, aircraft can get “under the weather,” which offers surveillance advantages; 

satellite data is macro, but near-earth data can be more tactical. sUAS also have limited 

detection capabilities. However, at wildfire system alert, a vertical takeoff sUAS could launch 

and provide an image and be out of area before a temporary flight restrictions area is declared 

and manned aircraft arrive. A systems engineering approach could determine how to bring 

forward all aerial and space assets such as those that collect hyperspectral and multi-spectral 

sensor data.

For surveillance during mitigation and suppression, thermal cameras that collect low pixel data 

could be mounted on air tankers or sensors added to sUAS or electric VTOL (eVTOL) vehicles if 

they are already flying resupply missions. The group also suggested dropping drones or similar 

hand-thrown devices from manned aircraft. Capabilities such as multi-view and multiperspective camera images are needed. It was also mentioned that better data collection is 

required on where fire retardant drops are made.

Aircraft are effective for high-resolution, wide area surveillance to conduct damage assessment 

of destroyed structures and enable classification of perimeters. However, smoke obscuration 

and strong updrafts affect daytime aviation capabilities. For pilots flying by visual flight rules,

night operations are not possible. For safety reasons, air operations are shut down at night. The 

exception is fire-mapping aircraft that fly in early morning hours when cooler backgrounds 

enable better discrimination. Unpiloted aircraft with terrain avoidance could better manage 

night-time operations. The best time to fight a fire is when it is laying down. Continuous aircraft 

firefighting operations would be beneficial.
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It could be useful to use larger, autonomous, unmanned or remotely operated aircraft with 

retardant dropping capability that can operate day and night and under low visibility especially 

if their engine configuration can avoid stalling out in really smoky air. For effectiveness, aircraft 

normally drop retardants from 150 feet at 100 – 121 knots. CAL FIRE operates turboprop 

aircraft that have little to no issues with the smoke ingestion and use an added water system 

for spraying water into the inlet to reduce power loss from smoke. A similar engine on an 

autonomous vehicle where the engine could be remotely monitored could be useful. Very large 

air tankers on the ridges are the best option for autonomous operations, but smaller air tankers 

or even swarms of sUAS may be more maneuverable and provide more rapid support especially 

during initial attack operations.

UAS operations are currently difficult for several reasons. Drones have limited battery time, 

operate poorly in bad weather conditions, and can have connectivity issues. However, drones 

fly slower and at lower elevations which could enable surveillance of focused areas of fire. In 

addition, drones can get into tighter places, are more expendable, and less expensive to 

operate. The National Defense Authorization Act (NDAA) has made UAS application more 

difficult with restrictions on certain foreign made drones and parts that currently dominate the 

market.

Integrated airspace operations with large aircraft and small drones is a challenge, especially 

with radios operating at different frequencies. Pilots need to feel confident in the other 

operators in the airspace. Improved communications between manned and unmanned assets, 

the ability to detect rogue actors, and deconfliction of airspace are examples of potential high 

priority solutions to operational challenges. In addition, sUAS must have consistent 

communication with their ground operators. Currently, if a sUAS loses ground contact, all 

operations are suspended or grounded until the connection can be re-established. 

There were several members in the breakout group who thought that a crawl, walk, run 

approach could allow earlier access to guidance tools (i.e., heads-up displays), augmented flight 

operations, and integrated sensors for pilots while large UAS platforms are brought into 

operational context. These incremental technology developments on manned aircraft could 

then be tested and moved to autonomous operations in a spiral development process.

Otherwise, development and certification could prolong access to tools.

Suppression and Mitigation 

There were seven breakout groups that focused on mitigation and suppression. The objective 

was to understand the primary challenges, provide some clarification on existing operations, 

and gather feedback on what breakthroughs or process flow changes could provide the 

greatest impact. It should be noted that some discussion points on mitigation and suppression 

concerned aerial operations. To avoid duplication, those topics are discussed in the preceding 

Aerial Fire Surveillance section.
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

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There was a call among participants for real-time, actionable data to assess the source of the 

fire risk and impact on the population, including rapid intervention for extraction. Any collected 

data needs to be aligned with existing data standards for tactical as well as strategic planning. 

Fire incidents do not have a fixed command and control structure for all fires and there are 

different response teams and coordination efforts depending on where the fire starts (i.e.,

local, state or federal land). Reducing the time between detection and fire classification can 

enable faster resource assessment and placement and safety prioritization.

Most useful could be technologies that provide rapid detection and situational awareness for 

suppression with 15-minute and potentially real-time updates. Persistent monitoring was 

emphasized to map escape routes and to understand encroachment factors, hot spots, critical 

infrastructure and community impact. Currently, there is gap between data collection and 

distribution. Ideally, firefighters want a map of the fire perimeter and how it is moving, state of 

fuels, and weather trends. The spatial and temporal resolution of the data is important; it can 

take 15 - 30 minutes to relocate if resources respond to the wrong location. Specifications 

range from 1- to 100-meter accuracy. 

Suggestions for LiDAR and spectrum data were mentioned as well as use of machine learning 

techniques to parse through information and display it in a useable manner. It was noted that 

intelligent solutions must include a confidence level and a low number of false alarms to be 

useful. In addition, deep learning has implications for infrastructure, where data will reside, and 

standardization for accessibility. Most importantly, data needs to be transferrable over phone 

networks in extreme conditions and in sometimes remote areas where communication is 

difficult. 

Data can be outdated by the time it is received due to long transmission times, and firefighters 

are already struggling to keep cellphone batteries alive all day. Updating an entire map, for 

instance, can take much longer than for a focused area of the map. Data for the Incident 

Command Center should be more detailed and broader than what the field operators need.

Another complication is the cascade of data from twitter feeds, cellphone video, and social 

media posts. Multiple data streams are being used to collect massive amounts of data and that 

can become confusing. As mentioned earlier, there are hopes that machine learning can 

process this information so that a cellphone could become a situational awareness tool.

Connectivity is also a challenge. Improved cellphone coverage is needed, especially on larger 

fires with incident command teams, and alerting mechanisms for low bandwidth and 

asynchronous connectivity would be helpful. Fire crews typically use very high frequency radios, 

but this can require airborne repeaters, and there can be interoperability issues between 

different communication systems.

Problems with sharing data can result from privacy issues. Firefighters are from both public and 

non-public entities. Sensor data from military sources can have sharing restrictions, and there 
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NASA Aeronautics Research Institute (NARI)

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are some initiatives to allow unlimited distribution rights. There are also privacy concerns 

associated with using mobile phone data or social media to determine people’s locations.

Smoothing out data formats, interoperability, and use of standard formats would simplify 

communications. Even simple solutions such as standardized color schemes across map 

products would result in quicker scans and decision-making. Firefighters need visual 

information. To be most useful, the research products need to be aligned with existing data 

standards (e.g., NWCG).

The need for cooperative public-private partnerships and developing revenue streams was 

stressed. The participants felt that there is a lack of quantitative analysis on the value of 

investing in new tactics. The private sector needs to develop an increased awareness of market 

potential. Overall, an alliance of researchers and operational teams in the field could contribute 

to effectively collecting observations for better coordination and technology development, 

including safe prototype testing. 

Post-fire Remedial Efforts

During the three breakout groups on post fire remedial efforts, areas identified for 

improvement, in addition to better access, included data collection methods, standardization of 

reporting, and methods of analyzing and using the data.

Inspections for erosion and post fire debris flow are done very quickly (even before the fire is 

contained) to assess post-fire threats from flooding, soil erosion, and instability. Depending on 

intensity, wildfire can change the chemistry of the soil and flash flood guidance. In mountainous 

regions, damaged vegetation and soil structure are inspected. NDVI products are used for 

mapping burn severity. The purpose is to assess land, damage, immediate rehabilitation, and 

long-term risk to groundwater and soil surfaces.

Multiple organizations collect this data. Burned Area Emergency Response (BAER)

9

teams 

typically conduct the inspections for the U.S. government, but BAER teams from different 

departments operate independently and California, for instance, uses the Watershed 

Emergency Response Team (WERT).10 BAER teams are dynamic in how they are formed so 

finding information later is based on the type of incident – depending upon whether it is a CAL 

FIRE, Forest Service, or a USGS topic, for instance. Cross-government collaboration is needed to 

harmonize and access this data more easily.

9 https://fsapps.nwcg.gov/baer/

10 https://www.bing.com/search?q=Watershed+Emergency+Response+Team+&form=QBLH&sp=-

1&pq=watershed+emergency+response+team+&sc=2-

34&qs=n&sk=&cvid=AA82212D34BA4C34B3124F1C9C10E32B
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NASA Aeronautics Research Institute (NARI)

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Satellite and airborne imagery are used to create Burned Area Reflectance Classification11

products, but timelines can be problematic depending on orbit schedule and cloud cover issues. 

Imagery taken by aircraft is also used to assess sediment level and debris flows but standards 

are needed to ensure all spectral data from aircraft and satellites is consistent to better enable 

burn severity algorithm development. 

Sensors that can obtain high-resolution optical imagery of burned areas through smoke and 

cloud cover in addition to hyper- and multi-spectral data are desired to assess how much cover 

is left to protect soils, if soils are hydrophobic, and to anticipate the effect of storms that may 

affect the watersheds during recovery. High resolution topography with detailed spatial scales 

would be valuable for modeling. Also, sensors to understand safety levels of chemical residuals

\toxicity of soils, and other hazards (e.g., old mines) are desired for determining when areas are 

safe to enter.

Remote detection of debris flow and erosion would reduce the time and resources needed for 

teams to gather this data daily as the fire progresses. Remote sensing to assess the quality of 

the soil is currently not possible. There has been great success with use of small, off-the-shelf 

drones, but many USGS assets have been grounded for post wildfire science due to the NDAA.

Timeliness of data is critical. Post assessment reports are published within a few days of the 

fire, requiring quick data acquisition and analysis. Distillation and machine learning techniques 

would benefit cross-agency users for future incidents. NASA developed case studies and rapid 

prototypes to support post fire remedial efforts, but there was no method to actually use the 

prototype. Comprehensive technology capability portfolios are lacking, which has fractured 

development and transition.

For analysis, pre-fire and post-fire imagery are needed for comparison. The Normalized Burn 

Ratio (NBR) index designed to highlight burnt areas in large fire zones. dNBR is the delta 

between pre-fire and post-fire NBRs. The USGS Landslide Hazards Program12 produces good 

models for pre- and post-fire debris flow probability and likelihood. USGS also does debris flow 

hazard modeling. Debris flows are channels that have a lot of dry sediment and are particularly 

dangerous to life and property. Continual collection of data and images for years after the fire is 

desired. Temporal data would help to understand land, vegetation, and watershed rates of 

recovery.

A collaborative hub where responding agencies and field agencies can operate is needed for 

better coordination and data sharing. A digital infrastructure could be developed to gather 

post-fire response datasets and support analysis for those impacted. Most post-fire assessment 

11 https://fsapps.nwcg.gov/baer/about-barc-faq

12 The primary objective of the National Landslide Hazards Program is to reduce long-term losses from 

landslide hazards by improving our understanding of the causes of ground failure and suggesting 

mitigation strategies.
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and remediation plans are only distributed among fire management entities. If they were 

public-facing, communities would be better informed of post-fire issues such as road closures, 

infrastructure damage, sensitive areas (keep out zones), and flood erosion potential areas. A 

public facing dashboard used in Florida helped communities recover and improve mitigation 

measures around their properties. Also, if parcel data is publicly shared, it could support fire 

management as well as rehabilitation of properties and road networks. 

Participants noted that overall, with the changes in the FAA (e.g., getting emergency 

Certificates of Waiver or Authorization has been more efficient and effective), and now, with 

the potential for greater NASA involvement, there is opportunity to improve today’s wildfire 

management operations.

Possible Next Steps

The Wildfire Management Workshop breakout sessions provided a wealth of information about 

the state of wildfire management and potential improvements. Much of the discussion focused 

not on better firefighting vehicles or tools but on creating useful data products that are 

delivered to front line personnel in a timely and effective manner. It is important to note that 

there was a recommendation to host another workshop with front line operational personnel

to gather additional feedback from their perspective. 

The subsequent possible next steps are derived from multiple facets of the Wildfire 

Management Assessment Activity, including the workshop discussions, Subject Matter Expert 

(SME) inputs, literature review, and RFI responses. They are classified into the following 

categories and aligned with NASA aeronautics capabilities. These are provided for further 

consideration by NASA and the broader wildfire community.

Comprehensive Concept of Operations, Systems Engineering Practice, and Transition to Reality

There is an opportunity to coordinate efforts to improve wildfire management. There could be 

great benefit in developing a multi-agency, unified concept of operations (ConOps) to align 

priorities, response strategies, information exchanges and integration, technologies and their 

interoperability requirements, and define a coordinated wildfire management approach. The 

ConOps could focus on near-term (about 3 years), mid-term (about 7 years), and longer-term 

activities (about 10 years). 

Systems engineering rigor could be used to identify needs of multiple types of users, map those

needs into functional requirements, and identify technology and procedure related 

considerations. This could result in a product roadmap that includes development of aircraft, 

airspace, communications, surveillance, and safety assurance technologies to ensure maximum 

impact for improving wildfire detection accuracy and timeliness, fire suppression effectiveness, 

and remedial efforts.
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NASA Aeronautics Research Institute (NARI)

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Transition from research and concepts into reality must be carefully planned. The users require 

mature, high technology readiness level products. If research and development are not 

sufficient, an approach must be created to support the transition of these efforts, culminating

in useful products. This would require coordination, collaboration, and partnerships among 

multiple government agencies, industry, and philanthropic and non-profit organizations to

successfully transition research results into reality at high technology readiness levels.

Leverage NASA Efforts and Programs

Both NASA’s Science and Space Technology Mission Directorates have programs that have

supported wildfire management efforts. NASA SMD, through the Earth Science Applied Sciences

Disasters Program has access to and controls multiple data assets and tools that are beneficial 

to wildfire management. NASA SBIR and STTR have previously invested in technologies from 

small businesses that support of wildfire management efforts and could continue to be 

leveraged. 

There could be benefit to collaboration among industry and other stakeholders in the

development and ultimate production of technologies for use by firefighters. To that end,

methods to incentivize industry could be beneficial. Other SBIR and STTR topics could 

encourage exploration of other concepts and technologies by small businesses and research 

institutions. STMD’s Prizes and Challenges Program could be used to incentivize industry and 

the public to innovate in wildfire management topics. Examples of competitions could include 

reliable, omnipresent communication systems and more effective, cheaper, and 

environmentally friendly fire retardant. Additionally, NASA could consider partnering with 

government and industry through a small satellite constellation for surveillance program or the 

GoFly Wildfire Competition currently under consideration to be released later this year. 

 

Data and Modeling

Data about wildfires comes from many sources including ground personnel, satellites, aircraft, 

and sUAS. Artificial intelligence (AI) and machine learning (ML) could be used in conjunction 

with the many data sources to produce effective data fusion. The data may not share a 

common format, which impedes processing. There is an array of modeling tools available to 

provide assessments and predictions of what a wildfire will do next. Improved projections using 

computational fluid dynamics, physics models, and supercomputers could greatly enhance the 

ability to anticipate and manage fires. This would make firefighting more efficient and help 

protect the safety of the firefighters, public, buildings, and infrastructure.

There could be benefit in conducting a comprehensive data and models inventory analysis to 

identify gaps and overlaps and review integration and interoperability considerations for 

multiple types of users. The purpose of this activity would be to maximize the value of 

information to the respective users at the time they need it the most. Existing modeling tools 
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NASA Aeronautics Research Institute (NARI)

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could be assessed, resulting in recommendations for improvements. The analysis could also 

cover how to increase the efficiency of processing data and running wildfire models. A goal 

would be to create integrated data collection and fire modeling systems that can be used for 

strategic analyses and fast-time tactical tools that provide information to firefighters on a 15- to 

30-minute refresh cycle.

Furthermore, data and models must be easy to use and available at the locations where various 

wildfire fighting personnel have to operate. 

Persistent Surveillance

Current surveillance of the fire regions could be enhanced from satellite-based (every 4 hours) 

to a combination of sensor data from satellite, small drones and High-Altitude Long Endurance 

(HALE) drone flights. There may be opportunities to augment and integrate SMD’s data sets 

with additional surveillance means to provide greater capability.

Organizational Considerations

There are many federal, state, county, municipal, and private organizations involved in wildfire 

prevention and management. Research is carried out by a range of universities and government 

entities. Participants in the workshop spoke of the difficulties in coordinating among the 

various groups in terms of funding important research, the timeliness of research findings, 

accessing new sources of data, formatting data sets for wildfire models, and getting 

information and information technology tools to fire fighters and the public at the right time.

Improved coordination and breaking down barriers that impede information flow could 

produce significant benefits.

There could be benefit in the establishment of a working group of representatives from the 

core wildfire organizations. The group could identify other key stakeholders and expand to 

include additional members, as needed. They would then address the perceived problems with 

coordination of data and resources that affect wildfire prevention and management. One

solution could be to form a new national coordinating body (or expand an existing one) to 

facilitate wildfire management and channel funding for research.

Wildfire Management Aircraft

Aircraft are used in wildfire management with great effectiveness. However, the situation could 

be improved. Aircraft could be equipped with sensors that collect data about the fire while they 

are on a water or flame-retardant drop mission. GPS devices could be used to pinpoint where 

the drop was made. Better air traffic control and coordination would allow earlier access to the 

fire site by large and small aircraft and improve safety. The role of sUAS could be increased to 

collect data before, during, and after the fire at the micro and macro levels. For example, front 

line personnel could be assigned a few sUAS that would be dispatched to areas of interest and 
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NASA Aeronautics Research Institute (NARI)

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controlled from a tablet device. Additionally, UASs could be used for active fire suppression by 

converting existing large tankers to be remotely piloted or sufficiently automated to make 

piloting optional.

As the scope, intensity, locations, and impact of wildfires have increased in the past decade, it is 

important to assess the aircraft fleet, their capabilities, and associated technologies that will be 

most effective. Relevant use cases include surveillance, suppression, and remedial inspections. 

A team could be formed to review firefighting aircraft in current use and recommend future 

developments in airframes, sensors, and flight deck automation. Experience with UTM could 

provide tested solutions for deeper involvement of sUAS in wildfire work. Sensor packages from 

NASA Earth Sciences could be reviewed for application on firefighting and surveying aircraft.

Flight deck automation could be specified to enable movement toward ground-piloted or 

autonomous tanker aircraft. The team could make recommendations, identify cutting-edge 

organizations, and locate potential funding sources. 

Persistent Communication 

Given that firefighters, incident commanders, and other personnel move around and operate in 

areas where current communications approaches are not always reliable, there could be 

benefits to developing concepts and technologies that will provide persistent communications.

Both voice and data communications would benefit wildfire management personnel in the 

field. As previously discussed, these personnel would benefit from having access to multiple 

data sources. Moreover, having access to secure and reliable internet capable of supporting 

high resolution data would also be necessary. NASA’s prior work in UAS Integration in the NAS 

project, and UAS Traffic Management (UTM) could be leveraged to develop requirements and 

technology options.

Cooperative Airspace Management for Manned and Unmanned Operations

A high workload activity during fire suppression is coordination of multiple types of manned 

and unmanned aircraft to ensure safe operations. NASA’s UTM, Scalable Traffic Management of 

Emergency Response Operations (STEReO), and in-time safety assessment methods could be 

leveraged to develop a roadmap for automated (e.g., UTM-in-a-box) airspace management 

technology to reduce this workload and enhance safety. The roadmap could include common 

situation awareness capability as an early step and fully automated self-contained airspace 

operations as the end goal. 

Wildfire Suppression Duration

Current practices largely confine the operations of helicopter and other aircraft to visual 

meteorological conditions, meaning that there is sufficient visibility for safe operations. There 

could be benefits to increase the suppression duration to as close to 24/7 as possible. 

Increasing the duration in day and night would likely require automated aircraft, automated 

airspace operations, and safety assurance methods to ensure persistent and safe operations. 
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NASA Aeronautics Research Institute (NARI)

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Supporting Firefighting Personnel 

Wildfire Workshop participants articulated that the firefighters at the fire site need pertinent, 

easy to interpret, and timely information to stop a fire in its early stages and combat it 

effectively. This would also protect the lives of the firefighting crews and the public.

Improvements in gathering data, running models, and collaborating between agencies could 

help in effective firefighting by providing the knowledge needed by the firefighters at the right 

place and time. Currently, information may not be available, be out of date, and/or not 

presented well. Cell phones and other networking tools often do not work in rural or forested 

areas.

A working group could be formed that includes project staff and representatives of front-line

firefighter organizations to create analyses and recommendations to build an awareness of 

what is needed at the fire’s edge. Sources for the critical information elements could be 

identified along with methods for communicating them such as HALE or new satellite 

constellations that offer Internet access. An early focus could be on assessing user needs and 

designing for the effective presentation of the data on hardened, portable devices.

Summary

This white paper captures highlights of the NASA ARMD Wildfire Management Workshop 

discussion and potential recommendations of capabilities to improve the state of the art. It is 

important to note that these recommendations are not considered prioritized or exhaustive, 

nor do they represent a consensus among participants. NASA looks forward to continued 

dialogue with the wildfire community on opportunities for collaboration.
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NASA Aeronautics Research Institute (NARI)

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Appendix A | NASA ARMD Wildfire Management Workshop Agenda
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Appendix B | Breakout Groups’ Questions

BREAKOUT SESSION 1

(Thursday, May 13, 2021 from 9:10AM – 10:10AM PT / 12:10PM – 1:10PM ET)

Planning for Fire Season (Group 1)

Breakout Questions: 

• Data Management: What are the main uses of data during wildfire preparedness? What 

are the key challenges for data use in wildfire planning/pre-fire season? 

• Indicate the source of data when answering the question. Data sources include 

airborne, satellite, and ground-based platforms.

• Is there a need for developing a standard for data and integration?

Planning for Fire Season (Group 2)

Breakout Questions: 

• Agency Coordination: How are new technologies tested and introduced into pre-fire 

season/planning operations today and how might this be better supported in the 

future?

• Where are the opportunities to improve planning across multiple agencies? 

Prediction Modeling and Challenges (Group 1)

Breakout Questions: 

• Data Management: What are the main uses of data during wildfire prediction? What 

data is required to support modeling? What are the key challenges for data use in 

wildfire prediction and modeling? 

• Indicate the source of data when answering the question. Data sources include 

airborne, satellite, and ground-based platforms.

• Is there a need for developing a standard for data and integration?

Prediction Modeling and Challenges (Group 2)

Breakout Questions: 

• Agency Coordination: How are new prediction and modeling technologies tested and 

introduced into prediction and modeling operations today and how might this be better 

supported in the future?

• Round Robin - Everybody identifies 3 areas for improvement and where research and 

development is needed.

Aerial Fire Surveillance (Group 1)

Breakout Questions: 

• What are typical challenges for aircraft that conduct fire surveillance operations? When 

is the aircraft surveillance better than satellite surveillance? Are there specific 

capabilities of aircraft and satellite you would like to have that don’t exist today?
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• What is the ideal range for aircraft for conducting surveillance of fires? What are the 

typical sensors (including their weight) installed on surveillance aircraft?

• Under what conditions (e.g., wind, visibility, day/night, altitude) do surveillance aircraft 

and their operations need to be conducted?

Suppression and Mitigation (Group 1) 

Breakout Questions: 

• Thinking through the process flow, where would a breakthrough make the greatest 

impact, e.g., around the clock operations capability, firefighters/personnel access to the 

fire, real-time information about heat detection and fire growth?

• What are current benchmark metrics (e.g., time to respond, time to put out fire, etc.)? 

What are barriers to meet those metrics and improve them?

Suppression and Mitigation (Group 2) 

Breakout Questions: 

• What are the key challenges for airspace and fleet operations management, e.g., 

aircraft, airfield, sensors, airspace?

• We have heard many cases where drones have caused stoppage of the aerial 

firefighting? What would you like to see changed so that wildfire aerial fighting is most 

effective?

• What are areas where research, development, testing, and aerial support could be 

improved? In what areas are research and development needed?

Suppression and Mitigation (Group 3)

Breakout Questions: 

• Thinking specifically about aviation wildfire response, what limits the effort to suppress 

fire from the air?

• What types of aircraft are currently used in wildfire suppression and mitigation? What 

sensors are currently used on these aircraft and what types of sensors and aircraft are 

planned for use in the future?

• Do all firefighting locations have similar access to aerial firefighting resources? How are 

the decisions made about which aircraft to use?

• What are areas where research, development, testing, and aerial support could be 

improved? In what areas are research and development needed?

Post-Fire Remedial Efforts (Group 1)

Breakout Questions: 

• What are typical activities conducted by following wildfire; why are they important? 

How can these activities of inspection, and analysis be improved?

• Agency Coordination: How are new technologies tested and introduced into postwildfire operations today and how might this be better supported in the future?
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Post-Fire Remedial Efforts (Group 2)

Breakout Questions: 

• Data Management: What are the main uses of data during wildfire recovery, damage 

assessment, and remediation determination? What data is required to support remedial 

efforts? What are the key challenges for data use in post-wildfire damage assessment 

and stabilization? 

• Should note the source of data when answering the question. Data sources include 

airborne, satellite, and ground-based platforms.

• Is there a need for developing a standard for data and integration?

• What are areas where research, development, testing, and aerial support could be 

improved? In what areas are research and development needed?

BREAKOUT SESSION 2

(Thursday, May 13, 2021 from 11:30AM – 12:30PM PT / 2:30PM – 3:30PM ET)

Planning for Fire Season (Group 1)

Breakout Questions: 

• How are lessons learned from previous fire season identified and captured? What are 

the opportunities of improvement in the development of lessons learned from the 

previous fire season and their application to improve next season? 

• Are lessons learned shared across the firefighting community? What are the barriers to 

sharing this information?

Planning for Fire Season (Group 2) 

Breakout Questions: 

• What are the most challenging/complex decision that wildfire management leaders 

must make? What makes them a challenge? What would be the most valuable type of 

support for these decisions (e.g., more/different data)?

• What are areas where research, development, testing, and aerial support could be 

improved? In what areas are research and development needed?

Prediction Modeling and Challenges (Group 1)

Breakout Questions: 

• What are the key challenges in the prediction of fire hazards? What are the key 

challenges in the prediction of day-to-day wildfire suppression and mitigation 

operations?

• Round Robin - Everybody identifies 3 areas for improvement and where research and 

development is needed.

Prediction Modeling and Challenges (Group 2)

Breakout Questions: 
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NASA Aeronautics Research Institute (NARI)

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• Identify data, analysis, and modeling needs by users/roles, e.g., researchers, planners, 

forecasters, firefighters, aerial firefighting pilots, incident commanders, virtual air boss, 

etc. 

• What areas do you recommend for improvement as related to prediction models, data, 

analysis, and presentation?

Aerial Fire Surveillance (Group 1) 

Breakout Questions: 

• How are wildfires detected today; what are the methods of surveillance? What's the 

biggest challenge around detection, e.g., sensor fidelity/sensitivity, false 

positives/negatives, sensor size/weight/power/cost. 

• What are different types of aircraft used in surveillance? How can that be improved? 

• Would it be useful to use larger autonomous, unmanned, or remotely operated aircraft 

which can operate day and night, and under low visibility (e.g., instrument 

meteorological conditions)

• What are the opportunities to improve communication and coordination as the fire is 

detected during aerial flights? What type of communications and streaming capabilities 

are needed for surveillance aircraft?

Suppression and Mitigation (Group 1) 

Breakout Questions: 

• Communication: Describe the role of communication in wildfire response? What are the 

key challenges with respect to communication?

• Identify communication needs by users, e.g., firefighters, pilots, aerial observers, 

incident commanders, virtual air boss, etc. Where are opportunities to improve 

communications systems during firefighting?

Suppression and Mitigation (Group 2)

Breakout Questions: 

• Data Management: What are the main uses of the data during wildfire response? What 

are the key challenges for data use in wildfire suppression and mitigation? 

• Note the source of data when answering the question. Data sources include airborne, 

satellite, and ground-based platforms.

• Is there a need for developing a standard for data and integration?

• Round Robin - Everybody identifies 3 areas for improvement and where research and 

development is needed.

Suppression and Mitigation (Group 3)

Breakout Questions:

• What are limitations of current operations (e.g., visual conditions, wind, daytime only, 

etc.)?
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

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• Would it be useful to use larger autonomous, unmanned, or remotely operated aircraft 

with retardant dropping capability which can operate day and night, and under low 

visibility (e.g., instrument meteorological conditions).

• What are barriers to 24/7 aerial firefighting?

Suppression and Mitigation (Group 4)

Breakout Questions: 

• Agency Coordination: How are new technologies tested and introduced into suppression

and mitigation operations today and how might this be better supported in the future?

• Do you see any opportunities to improve communication and coordination during 

firefighting based on multiple organizations and roles involved?

• Round Robin - Everybody identifies 3 areas for improvement and where research and 

development is needed.

Post-Fire Remedial Efforts (Group 1)

Breakout Questions: 

• What types of aircraft are currently used post-fire season? What sensors are currently 

used on these aircraft and what types of sensors and aircraft are planned for use in the 

future?

• Round Robin - Everybody identifies 3 areas for improvement and where research and 

development is needed.
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

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Appendix C | Breakout Groups’ Top 3 Outcomes

BREAKOUT SESSION 1

(Thursday, May 13, 2021 from 9:10AM – 10:10AM PT / 12:10PM – 1:10PM ET)

Planning for Fire Season (Group 1)

Breakout Top 3 Outcomes: 

1. Data sets that are sustainably updated are needed: 

• There are key types of data that are developed by third parties which do not 

guarantee persistence, commitment, and upkeep. 

• Cities, counties, state government, commercial, oil & gas pipelines, electric 

transmission lines, building footprints, maps of roads, structures, water/power 

infrastructure, fire weather, fuel moisture, vegetation. 

2. Improved network models are needed to predict impacts and fire scenarios:

• This would improve processes such as fire treatment planning, fire danger 

operating plans, and developing pre-planned fire scenarios for expected 

response. 

• Integrating these inputs with the Wildland Fire Decision Support System (WFDSS) 

would be very valuable.

3. Standards for data integration:

• Quality of data sets vary wildly across the country impacting modeling/planning 

efforts (e.g., vector datasets), sustained collaboration among the research 

community outside the fire season (e.g., data users and data providers). 

• Teaming with the National Wildfire Coordinating Group (NWCG) is important 

here. 

Planning for Fire Season (Group 2)

Breakout Top 3 Outcomes: 

1. Establish an interagency process template for State and Federal entities:

• Standardize rapid agreements and associated training/understanding to support 

wildfire activities.

• CalFire processes were cited as a model for this.

2. DoD and NASA understand how to assess situations and develop tactics/strategies. 

• Neither are members of National Wildfire Coordinating Group so formalizing 

partnerships with them will be valuable. 

• Partnership will help collaboration on physics-based modeling, other advanced 

computing techniques, and data collection/distribution techniques. 

3. Requirement for timely and actionable real-time information (real-time wind/weather 

predictions and detection, tracking, fire prediction):

• Information collection could also include predictive efforts such as monitoring 

tree mortality. 

• Rothaermel model needs to be updated based on today’s wildfire challenges. 
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

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• Collaborate on data collection techniques and advanced computing techniques 

could be beneficial.

• Consider leveraging other similar real-time processes and data solutions for 

disasters (e.g., volcanoes, earthquakes, floods, etc.).

Prediction Modeling and Challenges (Group 1)

Breakout Top 3 Outcomes: 

1. Data Update Rate Difficulties:

• Different time scales and density of data.

• Especially for fuel and localized weather which are important for modeling and 

prediction.

2. Data and Model Processing Times:

• Very computer intensive.

• Need to find a way to decrease processing time and ways to use more 

automated data analytics.

• Perhaps through data tagging and labelling.

3. Standardized Data Formats:

• Need open format for data exchange and interoperability to aid in automated 

data ingestion.

• There are too many sources that are proprietary (stove-piped data leads to login 

fatigue to get data to combine).

Prediction Modeling and Challenges (Group 2)

Breakout Top 3 Outcomes: 

1. Modelers need access to the best data available to develop and validate predictive 

tools. 

• There is currently little coordination among modelers and data 

producers/providers. 

• It would be very helpful for a university or similar entity to conduct a 

comprehensive survey of data sources and modeling efforts and publish the 

results. 

• It would be even better if this could be repeated periodically and maintained, 

and perhaps turned into a portal for use by the model development community.

2. Modelers should carefully consider the needs of the model users in the way they 

present their results. 

• Users are often non-scientific and cannot easily use raw data (like smoke density 

in PPM) to make decisions. 

• If possible, data should be presented in terms of risk (i.e., likelihood of causing 

damage, injury, etc.) to help users make more informed operational decisions.

3. The current SOA in fire behavior modeling is non-spatial (i.e., localized). 

• There is a great potential to improve the predictive capabilities of fire behavior 

models through use of spatial modeling tools like CFD. 
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

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• Computational power required will be high and will likely require the use of 

supercomputers.

Aerial Fire Surveillance (Group 1)

Breakout Top 3 Outcomes: 

1. ML/AI solutions for differentiating fire and combining data sets for better/reliable 

information.

• No or little AI/ML on camera collected information.

• Operationalization capability is lacking (multi-view, multi-perspective).

2. Common operating picture (integrated information, intel operator to figure out who to 

send to, operationalization of the product, geospatial information integration).

• Getting data off to users in real-time (amalgamation of concurrent systems).

• Need a coalition of partners to ensure integrated data distribution.

• Drop sensors, real time information flow is challenging (communications), 

policies, procedures, workflows don’t exist (no storage, display, and integrated 

perspective).

3. Wide area surveillance damage assessment.

• Aircraft offers advantage (getting under weather), small drones for surveillance 

(US made), most pyro luminous clouds, all weather conditions.

• Systems engineering approach to recognize how to bring forward all aerial and 

space assets, hyperspectral/multi-spectral sensor, persistence (fire guard, long 

endurance, GOES – system of systems solutions).

Suppression and Mitigation (Group 1) 

Breakout Top 3 Outcomes: 

1. Timely anticipation technologies:

• Provide rapid detection and situational awareness at decision scale with hourly 

or better updates toward near real time (with attention to specific 

actions e.g., morning and nighttime deployments).

• For suppression logistics tailored to fuel environment and season.

2. Initial attack, helping local fire practitioners prioritize and be efficient with resources,

including rapid intervention for extraction, deployment of retardants/water with 

autonomous aircraft and equipment. 

3. Rapid modeling and simulation for effective mitigation (prescribed burns and breaks):

• Supported by science imagery and analysis including for different fuel types and 

changing environmental conditions.

Suppression and Mitigation (Group 2) 

Breakout Top 3 Outcomes: 

1. Pilots in the airspace need to feel confident in the operators in the airspace.

• UAS’s need persistent communications to their ground operators. 
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

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• Currently if a UAS loses ground contact all operations are suspended or 

grounded till the contact can be re-established.

2. A persistent stare capability of <1m resolution is needed to provide knowledge at a 

human scale.

• Informs the “boots on the ground” (absolute knowledge of the theater).

3. Data fusion and merge capabilities from various sources is needed. 

• Fast, responsive, low data demand, way to permit ground users, coordinators, 

and the response team access to the global situation.

• More and more sensors are going into the air. 

Suppression and Mitigation (Group 3)

Breakout Top 3 Outcomes: 

1. Mapping and understanding the state of play. 

• Need: Night-time air tanker suppression activities (best time to fight a fire is 

when it's laying down).

• Need: Persistent surveillance and real-time/near real-time data (large gap 

between the time when information is collected and when people go out to fight 

the fire).

• Lots of opportunities for improvement with the frequency of fire mapping 

information and getting the info to the firefighters. 

• Too much can change in between data gathering times.

2. Different types of aircraft and usage techniques:

• Very Large Air Tankers (VLAT) on the ridges are the best option for autonomous 

operations.

• Need several size tankers (small for tight spots and larger for ridges).

• Air tankers outfitted with thermal cameras would allow them to provide fire 

status information. 

• Tankers should also record where drops are made.

3. The USGEO (multi-agency) Innovation task team is looking at making connections 

between needs and capabilities. 

• Need cooperative public-private partnerships and straight forward/easy ways to 

work together (Same issues 7 years later).

Post-Fire Remedial Efforts (Group 1)

Breakout Top 3 Outcomes: 

1. Post assessment reports such as the Burn Area Emergency Response (BAER) report are 

published within a few days requiring quick data acquisition and analysis.

• Distillation and/or a deep learning/AI aspect would benefit cross agency users 

and learning for future incidents. 

• Sensors for high resolution optical imagery of burned areas are needed and 

LiDAR as well as hyper and multispectral data are desired.
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

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• Sensor data or remote assessment on hazards (chemicals, old mines, etc.) is also 

needed to determine what areas are safe to enter.

2. Need a digital infrastructure to support post fire response datasets and analysis for 

impacted people.

• Use by multiple agencies and collaboration across gov levels, orgs.

• It was also noted that comprehensive technology capability portfolios are lacking 

leading to a fractured focus on technology development and transition.

3. Enhanced data need--temporal as well as spatial.

• Data acquisition also needed longer term on ground/space/aerial platforms to 

understand post incident impact on land/water rate of recovery.

Post-Fire Remedial Efforts (Group 2)

Breakout Top 3 Outcomes: 

1. Fire Burn Severity layers are key. 

• BAER Teams to support recovery assessment and model debris flow probability 

layers to effect hazard notifications to region / communities. 

• These layers are derived from Earth Observation (EO) data (Landsat and 

Sentinel), but timeliness of both can be problematic given their orbit schedule 

and cloud cover issues.

• Can we use other data to support those immediacy needs of data?

oContract aerial imagery services, other sensors with similar spectral 

bands to allow consistent Burn Severity algorithm development? 

oThere need to be “standards” for airborne acquisition of Burned Area 

data, so that Landsat / Sentinel and other spectral data (aircraft-acquired) 

can be consistent.

2. Most post-fire assessment and remediation plans are distributed for fire management 

use.

• Needs to be a public facing “dashboard” to allow communities to be informed of 

post-fire issues, such as road closures, damage to infrastructure, sensitive area 

(keep out zones), flood erosion potential areas, infrastructure damage.

• This would allow fire-affected communities have a means to understand the 

recovery or mitigation plans underway that will affect them. 

3. Each BAER team (from different agencies) provide their remediation/Burned Area 

Emergency Response (BAER) assessment reports to distributed service. 

• Need to coalesce those services for data continuity.

BREAKOUT SESSION 2

(Thursday, May 13. 2021 from 11:30AM – 12:30PM PT / 2:30PM – 3:30PM ET)

Planning for Fire Season (Group 1)

Breakout Top 3 Outcomes: 

1. Lessons learned format:
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

36

• Much is captured in paper form (e.g., Facilitated Learning Analysis, Incident 

Management Reports), but increasingly digital. 

• The opportunity is to increase digital capture, overcome barriers to sharing 

restricted digital data, and then utilize this for simulations and what-if learning 

for incident planning and management. 

2. Lessons learned are largely retrospective.

• Opportunities exist to be more anticipatory and pro-active, translating lessons 

into predictions while recognizing that that fires are now year around.

3. Lessons learned are being captured in part of the Wildfire Lesson Center over different 

time scales.

• Including heterogeneous data.

• Need to improve awareness to mobilize around improved plans, 

economic/financial impact, and incorporating earth systems, safety risk and loss 

assessments, and technology innovation for actionable plans.

Planning for Fire Season (Group 2) 

Breakout Top 3 Outcomes: 

1. Solutions are needed for Funding, Procurement, Data Merge / Operationalization, and 

Comms / distribution. 

2. The Fire Fighting community needs the state of the art from 2010 to be an operational 

capability in 2021. 

• “The state of the art is useless when you're all alone digging a fire ditch, unless 

it's in your pocket or in your pack."

3. Community building and coalitions are needed to break barriers between state, 

government, and private bodies. 

• Need the players (those fighting the fires) connecting to the R&D for practical 

maturation of technology for real world use now and in the future.

• The community is smaller than you expect and more distributed. 

o More community exchanges are needed to sit in a room and work out 

possible solutions. 

o More like this workshop where conversations are happening. We need 

operators to talk with short term technology infusers (12 - 24-month

solutions) to work out how Commercial Off-The-Shelf (COTS) tools can be 

applied today.

oIf this was a requirement / listening workshop, another focused 

workshop on partnerships and problem solving would be good. 

Prediction Modeling and Challenges (Group 1)

Breakout Top 3 Outcomes: 

1. Analysis of large amount of data in short time:

• May be able to benefit from Machine Learning (ML) algorithms for prediction 

modeling at various time scales.
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

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2. Sustained engagement between end users and research and development community 

to match development efforts to prioritized user needs. 

• For example, morning session identified need for IR wildfire detection, tracking, 

and projection.

3. Delivering appropriate information to the right people at the right time given 

constraints such as bandwidth.

• Communication very difficult in remote areas and extreme conditions.

Prediction Modeling and Challenges (Group 2)

Breakout Top 3 Outcomes: 

1. Need data at a higher spatial and temporal resolution (100m, 15-30min update) to be 

relevant to fast moving fires. 

• Data needs to be integrated across sources and users.

2. Need to know where the fire is, where the firefighting assets are, and where the public 

is to be able to assess risks and make operational decisions. 

• However, there are privacy issues associated with using mobile-phone data or 

social media to determine where people are.

3. It is a challenge for modelers to understand the requirements of operational people and 

to get technology into field operations. 

• Researchers want to be able to validate their technology in real world 

conditions, but fire fighters are rightfully concerned about testing low

Technology Readiness Level (TRL) technologies in life-threatening conditions.

Aerial Fire Surveillance (Group 1) 

Breakout Top 3 Outcomes: 

1. Detection: 

• Satellite, aircraft, and cameras (designing geostationary payload for school bus 

size detection) pixel dwell time.

2. King Air and Citation at National Infrared Operations (NIROPS) - 15000 ft and can see 

spot fire 8-inch diameter - not enough spatial resolution for mopup. It gets used for 

mopup, but that wasn’t what it was designed for.

• Designed and targeted for moving fires.

• Colorado multi mission a/c and others are also doing it – small UAS – need 

persistent surveillance. Small UAS would be good for mopup.

3. Small drones:

• Integrated airspace operations are a challenge, people operating radios at 

different frequencies.

Suppression and Mitigation (Group 1) 

Breakout Top 3 Outcomes: 

1. Multiple data streams are being used to collect massive amount of data. 
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

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• Only enough data to make an informed decision (any additional data can 

become confusing). 

• Can the computer/human interface be improved?

o Maybe machine learning to parse through the data and display it in a 

useable manner. 

2. Detect and mitigate drone interference with wildfire activities (e.g., rogue actors).

• Smoke/fire are challenges.

• What are the countermeasure technologies and implementation strategies that 

can be employed?

3. Can the communications between manned and unmanned assets be improved to allow 

for opportunities for collaboration across those assets?

Suppression and Mitigation (Group 2)

Breakout Top 3 Outcomes: 

1. Primary use: protecting life and property; Primary challenge: connectivity. 

2. Any collected data needs to be useful to the boots on the ground. 

• Additionally, in order to make collected data helpful to Geographic Information 

System (GIS) analysts and other uses, the research community needs to be 

properly educated/aligned with existing data standards (e.g., National Wildfire 

Coordination Group (NWCG)).

3. A challenging balancing act:

• High quality and infrequent vs. Acceptable quality and timelier. 

• Both have advantages, but which to use when? 

• More discussion is needed with more users/stakeholders to understand other 

such ‘knobs’ that represent important tradeoffs.

Suppression and Mitigation (Group 3)

Breakout Top 3 Outcomes: 

1. Current suppression mitigation operations (aerial) are limited by fire weather conditions 

(turbulence, visibility) and other elements (night / low-visibility flights, terrain, etc.); 

these necessitate improving developments of platforms and autonomous flight decision 

systems to allow 24/7 operations.

2. Since UAS fire suppression A/C may be of limited use now (due to platform design, 

build-out, and FAA certification time-lines), the team suggests a “spiral” development 

phase, where “augmented guidance (i.e., Heads-up displays for pilots) or optionally 

piloted (augmented flight ops) be the first stage of development (on manned platforms) 

while new UAS platforms are brought into operational context. These incremental 

technology developments on manned A/C can then be test-bedded and moved to 

autonomous ops in the spiral development stage.

3. Rather than focusing on “large” UAS Suppression (retardant drop) A/C, the focus can be 

on smaller platforms (rotorcraft (K-Max-class?) to support initial attack (IA) retardant 
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

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dropping. These platforms may be more maneuverable and provide more rapid support 

in those IA ops.

Suppression and Mitigation (Group 4)

Breakout Top 3 Outcomes: 

1. New physics-based models are starting to come online.

• Data intensive so data standardization and automated data input would be 

useful.

2. Need to get what is the most important data to the user based on the user's level.

• Data for the Incident Command Center will be more detailed and broader than 

what the field operators need ("high tech" vs "low tech" e.g., color movies vs 

pdf).

• This data needs to be communicated to the user just-in-time.

• Communicating data is difficult, especially to field operators in remote areas 

with spotty cell coverage; so, asynchronous connectivity and communications 

interoperability are needed.

3. Most see disaster response as a government function.

• Agencies must step up to support research, both private and public, in this area 

(DARPA for disaster response).

• Technology may exist but the governmental bureaucracy / institutional inertia 

may prevent it from being field tested in a timely manner (e.g., small UAS).

Post-Fire Remedial Efforts (Group 1)

Breakout Top 3 Outcomes: 

1. Great success with small off the shelf drones. 

• Looking at post-fire debris flows and flooding.

• Mostly use satellite and airborne imagery from others when available to create 

the Burned Area Reflectance Classification (BARC) products and burn severity 

layer aircraft imagery for sediment level assessment, debris flows, and flooding. 

• But need high resolution aircraft imagery data to assess areas at risk in addition 

to the other items above.

2. Are there partnerships/sponsorships to provide access to state teams doing remediation 

efforts in addition to the federal agencies? 

• Is there a way for state entities to get access to federal agency data not currently 

accessible to them? 

• Better coordination is needed (federal, state, and city governments) for data 

sharing.

3. Sentinel is 10m resolution, but with large-scale fires need better computing solutions for 

modeling. 

• A better hyperspectral data map for the Burned Area Emergency Response 

(BAER) teams to know how much cover is left to protect soils, are soils 
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

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hydrophobic, and what type of storms are going to hit their watersheds during 

recovery. 

• When is the watershed recovered? Could NASA resources (severity products) 

answer the question and assess the vegetation status? 

oInforms the communities how the risk level is reducing over time.
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

41

Appendix D |Possible Next Steps High-Level Descriptions

POSSIBLE NEXT STEP HIGH-LEVEL DESCRIPTION

Comprehensive Concept of 

Operations, Systems 

Engineering Practice, and 

Transition to Reality

• Develop multi-agency unified concept of operations to 

align priorities, response strategies, information exchanges 

and integration, technologies and their interoperability 

requirements, and coordinated wildfire management 

approach to maximize the impact.

• Use systems engineering approach to identify multiple 

types of users’ needs, map the users’ needs into functional 

requirements, and identify technology and procedure 

related considerations. This could result in a product 

roadmap for multiple users in the wildfire management 

community.

• Develop an approach to transition research and concepts 

into reality. The users need mature high technology 

readiness level products. This will require coordination and 

collaboration among multiple government agencies, 

industry, and non-profit organizations. 

Leverage NASA Efforts and 

Programs 

• Collaborate with NASA STMD for wildfire focused 

competitions, prizes, and SBIR/STTR topics. 

• Partner with NASA SMD to leverage data assets and tools 

that are beneficial to wildfire management

Data and Modeling • Conduct comprehensive data and models inventory 

analysis, identify gaps and overlaps, and identify integration 

and interoperability considerations for multiple types of 

users. This will maximize value of data and information to 

the respective users when they need it most.

Persistent Surveillance • Improve the surveillance of the fire regions by using a 

combination of sensor data from satellite and High-Altitude 

Long Endurance (HALE) flights.

Organizational 

Considerations 

• Improve coordination between federal, state, county, 

municipal, and private organizations involved in wildfire 

prevention and management, and break down barriers that 

impede information flow to produce significant benefits.

Wildfire Management 

Aircraft 

• Assess the needed aircraft fleet, their capabilities, and 

associated technologies that will be most effective in 

wildfire management. Relevant use cases include 

surveillance, suppression, and remedial inspections.

Persistent Communication • Develop concepts and technologies that will provide 

reliable, persistent communications (voice and data) in 

remote, hazardous areas.
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

42

POSSIBLE NEXT STEP HIGH-LEVEL DESCRIPTION

Cooperative Airspace 

Management for Manned 

and Unmanned Operations

• Develop roadmap for automated (e.g., UTM-in-a-box) 

airspace management technology. The roadmap could 

include common situational awareness capability as an 

early step and fully automated self-contained airspace 

operations as the end goal.

Wildfire Suppression 

Duration

• Increase the suppression duration in day and night (24/7) 

through automated aircraft, automated airspace 

operations, and safety assurance methods to ensure 

persistent and safe operations.

Support Firefighting 

Personnel 

• Provide the timely, accurate knowledge needed by the 

firefighters through improvements in gathering data, 

running models, and collaborating between agencies.
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

43

Appendix E | Breakout Groups’ Top 3 Outcomes and Possible Next Steps Crosswalk 

LEGEND: (of Possible Next Steps shorthand)

• Comprehensive Concept of Operations, Systems Engineering Practice, and Transition to 

Reality = Crosscutting

• Leverage NASA Efforts and Programs = NASA

• Data and Modeling = Data

• Persistent Surveillance = Surveillance 

• Organizational Considerations = Organizational

• Wildfire Management Aircraft = Aircraft

• Persistent Communication = Communication

• Cooperative Airspace Management for Manned and Unmanned Operations = Airspace

• Wildfire Suppression Duration = Suppression

• Supporting Firefighting Personnel = Personnel

Planning for Fire Season (Session 1: Group 1) 

Top 3 Outcomes Possible Next Steps 

Data sets that are sustainably updated are needed:

• There are key types of data that are developed by third 

parties which do not guarantee persistence, commitment, 

and upkeep. 

• Cities, counties, state government, commercial, oil & gas 

pipelines, electric transmission lines, building footprints, maps 

of roads, structures, water/power infrastructure, fire 

weather, fuel moisture, vegetation. 

Data, Organizational, 

Aircraft, Personnel 

Improved network models are needed to predict impacts and 

fire scenarios:

• This would improve processes such as fire treatment 

planning, fire danger operating plans, and developing preplanned fire scenarios for expected response.

• Integrating these inputs with the Wildland Fire Decision 

Support System (WFDSS) would be very valuable.

Data, Personnel 

Standards for data integration:

• Quality of data sets vary wildly across the country impacting 

modeling/planning efforts (e.g., vector datasets), sustained 

collaboration among the research community outside the fire 

season (e.g., data users and data providers). 

• Teaming with the National Wildfire Coordinating Group 

(NWCG) is important here. 

Data, Organizational
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

44

Planning for Fire Season (Session 1: Group 2) 

Top 3 Outcomes Possible Next Steps 

Establish an interagency process template for State and Federal 

entities:

• Standardize rapid agreements and associated 

training/understanding to support wildfire activities.

• CalFire processes were cited as a model for this.

Crosscutting, 

Organizational 

DoD and NASA understand how to assess situations and develop 

tactics/strategies. 

• Neither are members of National Wildfire Coordinating Group 

so formalizing partnerships with them will be valuable. 

• Partnership will help collaboration on physics-based 

modeling, other advanced computing techniques, and data 

collection/distribution techniques. 

Data, Organizational 

Requirement for timely and actionable real-time information 

(real-time wind/weather predictions and detection, tracking, 

fire prediction):

• Information collection could also include predictive efforts 

such as monitoring tree mortality. 

• Rothaermel model needs to be updated based on today’s 

wildfire challenges. 

• Collaborate on data collection techniques and advanced 

computing techniques could be beneficial.

• Consider leveraging other similar real-time processes and 

data solutions for disasters (e.g., volcanoes, earthquakes, 

floods, etc.).

Data, Surveillance, 

Organizational, NASA, 

Communication, Aircraft, 

Personnel 

Planning for Fire Season (Session 2: Group 1) 

Top 3 Outcomes Possible Next Steps 

Lessons learned format:

• Much is captured in paper form (e.g., Facilitated Learning 

Analysis, Incident Management Reports), but increasingly 

digital. 

• The opportunity is to increase digital capture, overcome 

barriers to sharing restricted digital data, and then utilize this 

for simulations and what-if learning for incident planning and 

management. 

Crosscutting, Data, 

Organizational 
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

45

Planning for Fire Season (Session 2: Group 1) 

Top 3 Outcomes Possible Next Steps 

Lessons learned are largely retrospective.

• Opportunities exist to be more anticipatory and pro-active, 

translating lessons into predictions while recognizing that that 

fires are now year around.

Data, Organizational 

Lessons learned are being captured in part of the Wildfire 

Lesson Center over different time scales.

• Including heterogeneous data.

• Need to improve awareness to mobilize around improved 

plans, economic/financial impact, and incorporating earth 

systems, safety risk and loss assessments, and technology 

innovation for actionable plans.

Crosscutting, Data, 

Organizational 

Planning for Fire Season (Session 2: Group 2) 

Top 3 Outcomes Possible Next Steps 

Solutions are needed for Funding, Procurement, Data Merge / 

Operationalization, and Comms / distribution. 

Crosscutting, Data, 

Surveillance, 

Organizational, Aircraft, 

Communication, 

Suppression, Personnel 

The Fire Fighting community needs the state of the art from 

2010 to be an operational capability in 2021. 

• “The state of the art is useless when you're all alone digging a 

fire ditch, unless it's in your pocket or in your pack.”

Crosscutting, Personnel 
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

46

Planning for Fire Season (Session 2: Group 2) 

Top 3 Outcomes Possible Next Steps 

Community building and coalitions are needed to break barriers 

between state, government, and private bodies. 

• Need the players (those fighting the fires) connecting to the 

R&D for practical maturation of technology for real world use 

now and in the future.

• The community is smaller than you expect and more 

distributed. 

• More community exchanges are needed to sit in a room and 

work out possible solutions. 

• More like this workshop where conversations are happening. 

We need operators to talk with short term technology 

infusers (12 - 24-month solutions) to work out how 

Commercial Off-The-Shelf (COTS) tools can be applied today.

If this was a requirement / listening workshop, another focused 

workshop on partnerships and problem solving would be good. 

Crosscutting, 

Organizational, 

Personnel 

Prediction Modeling and Challenge (Session 1: Group 1) 

Top 3 Outcomes Possible Next Steps 

Data Update Rate Difficulties:

• Different time scales and density of data.

• Especially for fuel and localized weather which are important 

for modeling and prediction.

Data, Surveillance, 

Aircraft

Data and Model Processing Times:

• Very computer intensive.

• Need to find a way to decrease processing time and ways to 

use more automated data analytics.

• Perhaps through data tagging and labelling.

Data

Standardized Data Formats:

• Need open format for data exchange and interoperability 

to aid in automated data ingestion. 

• There are too many sources that are proprietary (stovepiped data leads to login fatigue to get data to combine).

Data, Organizational 
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

47

Prediction Modeling and Challenges (Session 1: Group 2) 

Top 3 Outcomes Possible Next Steps 

Modelers need access to the best data available to develop and 

validate predictive tools: 

• There is currently little coordination among modelers and 

data producers/providers. 

• It would be very helpful for a university or similar entity to 

conduct a comprehensive survey of data sources and 

modeling efforts and publish the results. 

• It would be even better if this could be repeated periodically 

and maintained, and perhaps turned into a portal for use by 

the model development community.

Data

Modelers should carefully consider the needs of the model 

users in the way they present their results.

• Users are often non-scientific and cannot easily use raw data 

(like smoke density in PPM) to make decisions. 

• If possible, data should be presented in terms of risk (i.e., 

likelihood of causing damage, injury, etc.) to help users make 

more informed operational decisions.

Data, Organizational, 

Personnel

The current SOA in fire behavior modeling is non-spatial (i.e., 

localized). 

• There is a great potential to improve the predictive 

capabilities of fire behavior models through use of spatial 

modeling tools like CFD. 

• Computational power required will be high and will likely 

require the use of supercomputers.

Data

Prediction Modeling and Challenges (Session 2: Group 1) 

Top 3 Outcomes Possible Next Steps 

Analysis of large amount of data in short time:

• May be able to benefit from Machine Learning (ML) 

algorithms for prediction modeling at various time scales.

Crosscutting, Personnel
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

48

Prediction Modeling and Challenges (Session 2: Group 1) 

Top 3 Outcomes Possible Next Steps 

Sustained engagement between end users and research and 

development community to match development efforts to 

prioritized user needs. 

• For example, morning session identified need for IR wildfire 

detection, tracking, and projection.

Crosscutting, NASA, 

Data, Organizational, 

Personnel

Delivering appropriate information to the right people at the 

right time given constraints such as bandwidth.

• Communication very difficult in remote areas and extreme 

conditions.

Organizational, 

Surveillance, 

Communication, 

Personnel

Prediction Modeling and Challenges (Session 2: Group 2) 

Top 3 Outcomes Possible Next Steps 

Need data at a higher spatial and temporal resolution (100m, 

15-30min update) to be relevant to fast moving fires. 

• Data needs to be integrated across sources and users.

Data, Communication, 

Personnel

Need to know where the fire is, where the firefighting assets 

are, and where the public is to be able to assess risks and make 

operational decisions. 

• However, there are privacy issues associated with using 

mobile-phone data or social media to determine where 

people are.

Crosscutting, 

Surveillance, Aircraft, 

Communication, 

Organizational, 

Personnel

It is a challenge for modelers to understand the requirements of 

operational people and to get technology into field operations. 

• Researchers want to be able to validate their technology in 

real world conditions, but fire fighters are rightfully 

concerned about testing low Technology Readiness Level 

(TRL) technologies in life-threatening conditions.

Crosscutting, Data, 

Organizational, 

Personnel
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

49

Aerial Fire Surveillance (Session 1: Group 1) 

Top 3 Outcomes Possible Next Steps 

ML/AI solutions for differentiating fire and combining data sets 

for better/reliable information.

• No or little AI/ML on camera collected information.

• Operationalization capability is lacking (multi-view, multiperspective).

Crosscutting, 

Surveillance, 

Organizational, Data, 

NASA, Aircraft

Common operating picture (integrated information, intel 

operator to figure out who to send to, operationalization of the 

product, geospatial information integration).

• Getting data off to users in real-time (amalgamation of 

concurrent systems). 

• Need a coalition of partners to ensure integrated data 

distribution.

• Drop sensors, real time information flow is challenging 

(communications), policies, procedures, workflows don’t exist 

(no storage, display, and integrated perspective).

Crosscutting, 

Surveillance, 

Organizational, 

Communication

Wide area surveillance damage assessment.

• Aircraft offers advantage (getting under weather), small 

drones for surveillance (US made), most pyro luminous 

clouds, all weather conditions.

• Systems engineering approach to recognize how to bring 

forward all aerial and space assets, hyperspectral/multispectral sensor, persistence (fire guard, long endurance, GOES 

– system of systems solutions). 

Crosscutting, 

Surveillance, Aircraft

Aerial Fire Surveillance (Session 2: Group 1) 

Top 3 Outcomes Possible Next Steps 

Detection: 

• Satellite, aircraft, and cameras (designing geostationary 

payload for school bus size detection) pixel dwell time.

Surveillance, Aircraft
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

50

Aerial Fire Surveillance (Session 2: Group 1) 

Top 3 Outcomes Possible Next Steps 

King Air and Citation at National Infrared Operations (NIROPS) -

15000 ft and can see spot fire 8-inch diameter - not enough 

spatial resolution for mopup. It gets used for mopup, but that 

wasn’t what it was designed for.

• Designed and targeted for moving fires.

• Colorado multi mission a/c and others are also doing it – small 

UAS – need persistent surveillance. Small UAS would be good 

for mopup.

Surveillance, Aircraft

Small drones:

• Integrated airspace operations are a challenge, people 

operating radios at different frequencies. 

Aircraft, Airspace, 

Communication

Suppression Mitigation (Session 1: Group 1) 

Top 3 Outcomes Possible Next Steps 

Timely anticipation technologies:

• Provide rapid detection and situational awareness at decision 

scale with hourly or better updates toward near real time 

(with attention to specific actions e.g., morning and nighttime 

deployments).

• For suppression logistics tailored to fuel environment and 

season.

Data, Surveillance, 

Communication, 

Suppression, Personnel

Initial attack, helping local fire practitioners prioritize and be 

efficient with resources, including rapid intervention for 

extraction, deployment of retardants/water with autonomous 

aircraft and equipment. 

Organizational, 

Surveillance, NASA, 

Aircraft, Personnel

Rapid modeling and simulation for effective mitigation 

(prescribed burns and breaks):

• Supported by science imagery and analysis including for 

different fuel types and changing environmental conditions 

Data, Surveillance, NASA, 

Aircraft, Personnel
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

51

Suppression Mitigation (Session 1: Group 2) 

Top 3 Outcomes Possible Next Steps 

Pilots in the airspace need to feel confident in the operators in 

the airspace. 

• UAS’s need persistent communications to their ground 

operators. 

• Currently if a UAS loses ground contact all operations are 

suspended or grounded till the contact can be re-established.

Crosscutting, Data, 

Surveillance, 

Communication, 

Suppression, Personnel

A persistent stare capability of <1m resolution is needed to 

provide knowledge at a human scale.

• Informs the “boots on the ground” (absolute knowledge of 

the theater).

Aircraft

Data fusion and merge capabilities from various sources is 

needed. 

• Fast, responsive, low data demand, way to permit ground 

users, coordinators, and the response team access to the 

global situation.

• More and more sensors are going into the air. 

Crosscutting, NASA, 

Organizational

Suppression Mitigation (Session 1: Group 3) 

Top 3 Outcomes Possible Next Steps 

Mapping and understanding the state of play. 

• Need: Night-time air tanker suppression activities (best time 

to fight a fire is when it's laying down). 

• Need: Persistent surveillance and real-time/near real-time 

data (large gap between the time when information is 

collected and when people go out to fight the fire). 

• Lots of opportunities for improvement with the frequency of 

fire mapping information and getting the info to the 

firefighters. 

• Too much can change in between data gathering times.

Crosscutting, NASA, 

Organizational
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

52

Suppression Mitigation (Session 1: Group 3) 

Top 3 Outcomes Possible Next Steps 

Different types of aircraft and usage techniques:

• Very Large Air Tankers (VLAT) on the ridges are the best 

option for autonomous operations.

• Need several size tankers (small for tight spots and larger for 

ridges). 

• Air tankers outfitted with thermal cameras would allow them 

to provide fire status information. 

Tankers should also record where drops are made. 

Aircraft

The USGEO (multi-agency) Innovation task team is looking at 

making connections between needs and capabilities. 

• Need cooperative public-private partnerships and straight 

forward/easy ways to work together (Same issues 7 years 

later).

Crosscutting, NASA, 

Organizational

Suppression Mitigation (Session 2: Group 1) 

Top 3 Outcomes Possible Next Steps 

Multiple data streams are being used to collect massive amount 

of data. 

• Only enough data to make an informed decision (any 

additional data can become confusing). 

• Can the computer/human interface be improved?

• Maybe machine learning to parse through the data and 

display it in a useable manner. 

Data, Surveillance, 

Communication, 

Personnel

Detect and mitigate drone interference with wildfire activities 

(e.g., rogue actors).

• Smoke/fire are challenges.

• What are the countermeasure technologies and 

implementation strategies that can be employed?

Aircraft, Airspace 

Can the communications between manned and unmanned 

assets be improved to allow for opportunities for collaboration 

across those assets? 

Crosscutting, Aircraft, 

Communication
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

53

Suppression Mitigation (Session 2: Group 2) 

Top 3 Outcomes Possible Next Steps 

Primary use: protecting life and property; Primary challenge: 

connectivity. 

Crosscutting, 

Communication, 

Personnel

Any collected data needs to be useful to the boots on the 

ground. 

• Additionally, in order to make collected data helpful to 

Geographic Information System (GIS) analysts and other uses, 

the research community needs to be properly 

educated/aligned with existing data standards (e.g., National 

Wildfire Coordination Group (NWCG)).

Data, Communication, 

Personnel

A challenging balancing act:

• High quality and infrequent vs. Acceptable quality and 

timelier. 

• Both have advantages, but which to use when? 

• More discussion is needed with more users/stakeholders to 

understand other such ‘knobs’ that represent important 

tradeoffs.

Aircraft, Suppression, 

Personnel

Suppression Mitigation (Session 2: Group 3) 

Top 3 Outcomes Possible Next Steps 

Current suppression mitigation operations (aerial) are limited by 

fire weather conditions (turbulence, visibility) and other 

elements (night / low-visibility flights, terrain, etc.); these 

necessitate improving developments of platforms and 

autonomous flight decision systems to allow 24/7 operations.

Aircraft, Suppression, 

Personnel

Since UAS fire suppression A/C may be of limited use now (due 

to platform design, build-out, and FAA certification time-lines), 

the team suggests a “spiral” development phase, where 

“augmented guidance (i.e., Heads-up displays for pilots) or 

optionally piloted (augmented flight ops) be the first stage of 

development (on manned platforms) while new UAS platforms 

are brought into operational context. These incremental 

technology developments on manned A/C can then be testbedded and moved to autonomous ops in the spiral 

development stage.

Crosscutting, Aircraft, 

Airspace
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

54

Suppression Mitigation (Session 2: Group 3) 

Top 3 Outcomes Possible Next Steps 

Rather than focusing on “large” UAS Suppression (retardant 

drop) A/C, the focus can be on smaller platforms (rotorcraft (KMax-class?) to support initial attack (IA) retardant dropping. 

These platforms may be more maneuverable and provide more 

rapid support in those IA ops.

Data, Aircraft

Suppression Mitigation (Session 2: Group 4) 

Top 3 Outcomes Possible Next Steps 

New physics-based models are starting to come online.

• Data intensive so data standardization and automated data 

input would be useful.

Data, Organizational

Need to get what is the most important data to the user based 

on the user's level.

• Data for the Incident Command Center will be more detailed 

and broader than what the field operators need ("high tech" 

vs "low tech" e.g., color movies vs pdf).

• This data needs to be communicated to the user just-in-time.

• Communicating data is difficult, especially to field operators 

in remote areas with spotty cell coverage; so, asynchronous 

connectivity and communications interoperability are needed.

Data, Organizational, 

Communication, 

Personnel

Most see disaster response as a government function.

• Agencies must step up to support research, both private and 

public, in this area (DARPA for disaster response).

• Technology may exist but the governmental bureaucracy / 

institutional inertia may prevent it from being field tested in a 

timely manner (e.g., small UAS).

Crosscutting, 

Organizational, 

Personnel
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

55

Post-Fire Remedial Efforts (Session 1: Group 1) 

Top 3 Outcomes Possible Next Steps 

Post assessment reports such as the Burn Area Emergency 

Response (BAER) report are published within a few days 

requiring quick data acquisition and analysis. 

• Distillation and/or a deep learning/AI aspect would benefit 

cross agency users and learning for future incidents. 

• Sensors for high resolution optical imagery of burned areas 

are needed and LiDAR as well as hyper and multispectral 

data are desired. 

• Sensor data or remote assessment on hazards (chemicals, old 

mines, etc.) is also needed to determine what areas are safe 

to enter. 

Data, Organizational

Need a digital infrastructure to support post fire 

response datasets and analysis for impacted people.

• Use by multiple agencies and collaboration across gov levels, 

orgs.

• It was also noted that comprehensive technology capability 

portfolios are lacking leading to a fractured focus on 

technology development and transition. 

Crosscutting, NASA, 

Data, Organizational

Enhanced data need--temporal as well as spatial.

• Data acquisition also needed longer term on 

ground/space/aerial platforms to understand post incident 

impact on land/water rate of recovery.

Data
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

56

Post-Fire Remedial Efforts (Session 1: Group 2) 

Top 3 Outcomes Possible Next Steps 

Fire Burn Severity layers are key. 

• BAER Teams to support recovery assessment and model 

debris flow probability layers to effect hazard notifications to 

region / communities. 

• These layers are derived from Earth Observation (EO) data 

(Landsat and Sentinel), but timeliness of both can be 

problematic given their orbit schedule and cloud cover 

issues. 

• Can we use other data to support those immediacy needs of 

data? 

• Contract aerial imagery services, other sensors with similar 

spectral bands to allow consistent Burn Severity algorithm 

development? 

• There need to be “standards” for airborne acquisition of 

Burned Area data, so that Landsat / Sentinel and other 

spectral data (aircraft-acquired) can be consistent.

Data, NASA, 

Organizational, Aircraft

Most post-fire assessment and remediation plans are 

distributed for fire management use.

• Needs to be a public facing “dashboard” to allow 

communities to be informed of post-fire issues, such as road 

closures, damage to infrastructure, sensitive area (keep out 

zones), flood erosion potential areas, infrastructure damage.

• This would allow fire-affected communities have a means to 

understand the recovery or mitigation plans underway that 

will affect them. 

Data, Organizational

Each BAER team (from different agencies) provide their 

remediation/Burned Area Emergency Response (BAER) 

assessment reports to distributed service. 

• Need to coalesce those services for data continuity.

Data, Organizational
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

57

Post Fire Remedial (Session 2: Group 1) 

Top 3 Outcomes Possible Next Steps 

Great success with small off the shelf drones. 

• Looking at post-fire debris flows and flooding. 

• Mostly use satellite and airborne imagery from others when 

available to create the Burned Area Reflectance Classification 

(BARC) products and burn severity layer aircraft imagery for 

sediment level assessment, debris flows, and flooding. 

• But need high resolution aircraft imagery data to assess areas 

at risk in addition to the other items above.

Data, Aircraft, 

Organizational

Are there partnerships/sponsorships to provide access to state 

teams doing remediation efforts in addition to the federal 

agencies? 

• Is there a way for state entities to get access to federal agency 

data not currently accessible to them? 

• Better coordination is needed (federal, state, and city 

governments) for data sharing. 

NASA, Organizational,

Data

Sentinel is 10m resolution, but with large-scale fires need better 

computing solutions for modeling. 

• A better hyperspectral data map for the Burned Area 

Emergency Response (BAER) teams to know how much cover is 

left to protect soils, are soils hydrophobic, and what type of 

storms are going to hit their watersheds during recovery. 

• When is the watershed recovered? Could NASA resources 

(severity products) answer the question and assess the 

vegetation status?

• Informs the communities how the risk level is reducing over 

time.

NASA, Data
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

58

Appendix F | Breakout Groups’ Top 3 Outcomes and Possible Next Steps Crosswalk Plots 

LEGEND: (of Possible Next Steps shorthand)

• Comprehensive Concept of Operations, Systems Engineering Practice, and Transition to 

Reality = Crosscutting

• Leverage NASA Efforts and Programs = NASA

• Data and Modeling = Data

• Persistent Surveillance = Surveillance 

• Organizational Considerations = Organizational

• Wildfire Management Aircraft = Aircraft

• Persistent Communication = Communication

• Cooperative Airspace Management for Manned and Unmanned Operations = Airspace

• Wildfire Suppression Duration = Suppression

• Supporting Firefighting Personnel = Personnel
Dr. Parimal Kopardekar; Laurie Grindle

NASA Aeronautics Research Institute (NARI)

NASA ARMD Wildfire Management Workshop

59