Neal on Fire

Student researcher Rhea Rawat joins Neal on Fire to explain her patented approach to wildfire detection using buried soil temperature and moisture sensors. Learn how underground conditions change when fire passes overhead, how sensor depth affects detection time and reliability, and how wireless sensor networks could complement cameras and satellites to improve early detection.

Host Neal Spungen speaks with Rhea Rawat about her wildfire detection research, published in the Journal of Emerging Investigators. She explains how soil temperature and moisture respond to surface fires, what her experiments revealed, how her prototype communicates, and what is needed to move from proof of concept to real-world deployment.

Rhea Rawat is a student researcher who developed a wireless network of underground soil sensors for wildfire detection. Her research was published in the Journal of Emerging Investigators, and her approach has been patented.

• Limitations of above-ground detection tools, including false alarms and equipment damage.
• How fire affects underground soil temperature and moisture.
• Why sensor depth influences detection speed and reliability.
• Experiments using sensors at one-inch and three-inch depths, with temperature thresholds of 20% and 30%.
• Detection times of approximately 10–17 minutes in controlled tests.
• The trade-off between detection speed and accuracy.
• The prototype’s waterproof DS18B20 temperature sensors, capacitive moisture sensors, and LoRa wireless communication.
• How sensor nodes transmit readings to a base station approximately every 15 seconds.
• Power requirements and challenges of scaling the network.
• Why underground sensing may need to complement cameras, satellites, and other detection technologies, particularly for canopy fires.
• Experimental results showing no false alarms or missed detections.
• An algorithm that detects rapid changes in readings to reduce the influence of soil type.
• Sensor durability and deployment challenges, including cost, power, communications, and placement.
• Future field testing across different soil types, vegetation, and climates.
• Controlled fire simulations using heated coal.
• Patent coverage and potential partnerships with research organizations, agencies, and utilities.

1. Surface fires create measurable changes in underground soil temperature and moisture.
2. Buried sensors offer protection from direct flames and embers but must withstand harsh conditions.
3. Sensor depth and detection thresholds affect speed and reliability.
4. Integrating ground sensors with other technologies could improve detection capabilities.
5. Strategic placement in high-risk areas could support wider deployment.
6. Large-scale field testing is the next step toward real-world use.

Rhea’s Research: https://emerginginvestigators.org/articles/24-154

Martin Hofmann, CEO of HOZE Solutions and a former hotshot crew member, joins Neal on Fire to explain FireFence, an unmanned, ground-based system that replicates aerial water drops to slow or contain a fire front using far less water. Learn how it works, how prescribed burns are used for real-fire testing, and how HOZE’s Defensible Space Viewer helps asset owners meet new European compliance rules.

Show Notes

Episode summary
Host Neal Spungen talks with Martin Hofmann, CEO of HOZE Solutions, about FireFence, an unmanned containment system designed to use water far more efficiently, and the Defensible Space Viewer, a tool that helps asset owners understand and document defensible space requirements. Martin also discusses prescribed burn testing and the need for reference standards for wildfire equipment.

About the guest
Martin Hofmann is the CEO of HOZE Solutions, based in Germany. He co-founded the company with David after the two worked on a hotshot crew in Greece, where they regularly ran short of water. His background is in engineering and academia.

Topics covered

• The origin story: running out of water on a hotshot crew in Greece
• FireFence: an optimized long-distance soaker hose that replicates aerial drops on the ground
• More than five years of development, including experiments at the University of Coimbra and prescribed burns in Portugal as a testing platform
• Why it is unmanned: crews install it ahead of the fire and retreat to safety
• Treating 800 meters in one run before a relay pump is needed
• Droplet size, drift, and using moisture in the air to counter embers
• Adjusting droplet size through chemistry, plus wind tunnel testing and onboard sensors
• A software tool that turns a line drawn on a map into a required run time
• Deployment from pickup trucks, with robotic and helicopter options explored
• About one kilometer treated per truckload in moderate conditions
• A self-wetting hose that can sustain flame contact, and limits such as very high fire line intensity and crown fires
• Seasonal installations for large assets such as solar parks
• Water versus retardants, and the need for reference tests of firefighting equipment
• The Defensible Space Viewer, laws in France and Greece, and the role of insurance
• The European Space Agency program and machine learning to label risk drivers
• Go-to-market plans and planned burns in Belgium, Spain, and Portugal

Key takeaways

1. An unmanned, ground-based system keeps crews out of the danger zone while still affecting the fire front.
2. Water efficiency and droplet design matter, especially for managing embers.
3. Real-fire testing is critical, and reference standards are missing for much wildfire equipment.
4. Defensible space is becoming a legal requirement in parts of Europe.
5. Shared responsibility with neighbors complicates compliance.
6. Collaboration between practitioners, researchers, and companies can speed preparedness.

Resources
https://hozesolutions.com/
https://nealonfire.com/

Holly Eagleston, CEO and co-founder of firescape.ai and former Sandia National Laboratories scientist, joins Neal on Fire to explain how Firescape combines satellite imagery, weather data, AI, and wildfire simulations to deliver circuit-level risk scores for electric utilities. Learn how the platform supports public safety power shutoff (PSPS) decisions, non-reclosing settings, and safe re-energization, why granular risk data can reduce unnecessary outages, and how emerging satellites are improving wildfire situational awareness.

SHOW NOTES

Episode Summary

Host Neal Spungen talks with Holly Eagleston about turning complex wildfire science into actionable intelligence for electric utilities. Firescape integrates satellite imagery, weather forecasts, and utility asset data into a unified risk score, helping utilities determine where to apply mitigation measures, when power shutoffs may be necessary, and how to restore power safely.

About the Guest

Holly Eagleston is the CEO and co-founder of firescape.ai. She spent approximately six years at Sandia National Laboratories, leading scientists and engineers in developing wildfire risk models and remote sensing tools for Department of Energy-funded energy security projects.

Topics Covered

• How Holly’s experience at Sandia, work with utilities, and the Smokehouse Creek fire inspired Firescape.
• Making wildfire science accessible to utilities without in-house meteorology or data science teams.
• Combining satellite imagery, weather data, wildfire simulations, and AI to generate consequence-based risk scores.
• Providing email alerts up to seven days before potential events, with data refreshed hourly.
• Integrating multiple data sources into one platform for easier wildfire risk monitoring.
• Using multispectral satellite imagery, HRRR, GFS, European weather models, and ground weather stations.
• Downscaling wind data from 3 km to approximately 50 meters using terrain-aware modeling.
• Supporting decisions before, during, and after wildfires using dispatch data, satellite thermal observations, and fire spread forecasts.
• Applying non-reclosing settings to individual reclosers instead of entire red flag warning zones.
• Using random forests and convolutional neural networks (CNNs) to create detailed data layers, including fuel moisture.
• Integrating alerts, web tools, and future SCADA-enabled workflows while keeping humans involved in critical decisions.
• How FireSat and hyperspectral sensors could improve wildfire detection and situational awareness.
• Data challenges, collaboration with Los Alamos National Laboratory, and launching the platform in approximately three months.
• Why utility wildfire mitigation requires multiple strategies, including grid hardening, undergrounding, covered conductors, and public safety power shutoffs.

Key Takeaways

1. Wildfire risk intelligence is increasingly important for electric utilities across the United States.
2. Circuit-level assessments can reduce unnecessary outages while addressing ignition risks.
3. Integrating multiple data sources simplifies wildfire monitoring and decision-making.
4. Firescape uses machine learning with satellite and ground data, not generative AI or large language models.
5. Better satellite technology will improve wildfire detection and forecasting.
6. Effective mitigation requires multiple strategies, not just power shutoffs.

Resources

Firescape.ai: https://www.firescape.ai/home
Neal on Fire: https://nealonfire.com/

Dr. Supratik Mukhopadhyay of Louisiana State University joins Neal on Fire to explain DeepFire, a multi-agent AI system that predicts where wildfires are likely to start, detects smoldering fires and early ignitions from satellite data, and tracks spread. Learn how it forecasts risk from seven to 45 days ahead, uses 30 environmental datasets, confirms detections with truck-mounted cameras, and keeps humans in the loop as an augmentation tool for fire agencies and utilities.

Show Notes

Episode summary
In this episode of Neal on Fire, host Neal Spungen talks with Dr. Supratik Mukhopadhyay, whose research at Louisiana State University connects artificial intelligence, satellite data, and a wide range of environmental datasets to forecast wildfire risk before ignition. He explains how DeepFire works, how it is validated, where it is being deployed, and what upgrades are planned.

About the guest
Dr. Supratik Mukhopadhyay is a researcher at Louisiana State University with more than 20 years of experience in artificial intelligence. He has worked with NASA on projects including high-resolution land cover maps of the continental US at one-meter resolution, work that led to his team’s interest in wildfires around 2018. DeepFire is designed to detect both smoldering fires and active flame ignitions, not just fully developed wildfires.

Topics covered
* What drew him to wildfire research, and why detection accuracy and late detection were major gaps
* How DeepFire works as a set of cooperating agents: prediction, detection, spread tracking, and confirmation
* Prediction at several time scales, from seven to 45 days, at more than 90% accuracy by his account
* How the detection agent uses predictions to know where and when to look, and why this supports early action and resource staging
* Using land cover maps and tree canopy changes as a proxy for brush accumulation beneath the canopy
* Validation with historical data and hindcasts
* Handling false positives and missed detections with a truck-mounted camera confirmation system with long-range cameras
* Out-of-distribution detection, which flags when the system sees a pattern it has not seen before
* Current coverage in the western US, western Canada, Indonesia, and Australia, with Greece and South America as next targets
* Licensing to Firemark AI in Calgary, with funding support from Alberta Innovates
* Positioning DeepFire as augmentation for human decision-makers, not a replacement
* Transfer learning and the need for fine-tuning across land cover, geography, and ocean currents
* Future enhancements: synthetic aperture radar to see through clouds, AI running in space, ground-based sensors, and more powerful AI
* Satellite data latency of up to about 1.5 hours
* Related geospatial work at LSU, including Arctic permafrost degradation

Key takeaways
1. Predicting where fires are likely to start lets agencies stage scarce resources and do preventive work before ignition.
2. A multi-agent design allows prediction, detection, and spread tracking to inform each other in real time.
3. Humans remain in the loop, since accuracy is high but not perfect.
4. Models need local fine-tuning because ecosystems and geography differ, even within one region.
5. Cloud cover and satellite latency are key limits, which radar and onboard AI in space could help address.
6. Ground sensors could help but are costly across vast areas like Alberta.

Resources
https://nealonfire.com/

Dr. Ryan Alimo, founder of Opal AI and a former NASA JPL scientist, joins Neal on Fire to explain how physical AI, 3D digital twins, and multimodal data are helping fire agencies and utilities assess wildfire and wildland-urban interface (WUI) risk. Learn how FireVision combines real-time fuel maps, a dynamic fire potential index, a WUI wildfire simulator, and an AI decision support tool to guide resource staging, power shutoff decisions, and evacuation planning.

Show Notes

Episode summary
In this episode of Neal on Fire, host Neal Spungen talks with Dr. Ryan Alimo, founder of Opal AI, about applying “physical AI” to wildfire and urban risk. Ryan explains how the company builds 3D digital twins of communities, combines them with satellite, aerial, weather, and sensor data, and produces risk insights that help fire agencies and utilities make faster decisions before, during, and after a fire.

About the guest
Dr. Ryan Alimo is the founder of Opal AI, based in Los Angeles. He is a former NASA JPL scientist who led deep learning work and a wildfire AI initiative at JPL. Opal AI is a spin-off from NASA JPL and UCLA, and has received NASA funding.

Topics covered
* Why large language models struggle with the real world, and what “physical AI” means for fire and emergency response
* Creating 3D digital twins of urban areas using LiDAR, cameras, and street-level data
* Who uses the tools: fire chiefs, emergency managers, utility wildfire risk teams, HOAs, developers, and homeowners
* Planning uses (vegetation management, city and evacuation planning) and day-to-day uses (such as public safety power shutoff decisions)
* Combining ground stations, weather, satellite, aerial, street view, and historical fire data in a foundation model
* The human and AI teaming approach, with analysts asking the AI questions
* Prioritizing assets and areas for the biggest risk reduction
* Forecasting fire potential up to 48 hours ahead, including an analysis of the Palisades area
* NASA funding and collaboration, plus support from Nvidia’s Inception program, Google Cloud, and Amazon
* Where AI fits as decision support, and why communication between agencies and with the public remains human
* Validating results with historical fires and fire simulators
* The challenges of pilot projects and government procurement
* Training on public data sources such as camera feeds, with fine-tuning for each hardware setup
* FireVision’s four components: real-time fuel maps (FuelVision), a dynamic risk score (the Opal fire potential index), a WUI wildfire simulator, and a natural language decision support tool
* Fuel maps updated every few days at under 30 meters, compared with updates that can take months or years
* Coupling existing wildland simulators with Opal’s WUI simulator, and peer-reviewed research
* Highlights of his NASA work, including reducing the workload of Mars rover data analysis

Key takeaways
1. Physical AI aims to work with real-world data such as LiDAR, sensors, and weather, not just text and images.
2. Digital twins of the built environment help analyze risk in the WUI, where wildland tools often fall short.
3. Utilities can use risk prioritization for decisions such as vegetation work and power shutoffs.
4. Human-AI teaming combines fast data processing with expert judgment.
5. Pilots and procurement, not technology, are often the biggest bottleneck for adoption.
6. Opal AI says no single tool has solved wildfire risk, and it is open to collaboration.

Resources
https://www.opal-ai.com/
https://nealonfire.com/

Melvin Sieben, CEO and founder of Mejor Technologies, joins Neal on Fire to explain how CHRIS Cloud transforms drone imagery into live, geo-referenced wildfire maps for first responders, while Lucy predicts wildfire susceptibility at 10-meter resolution up to 14 days ahead. Learn how off-grid technology keeps mapping operational without power or mobile coverage, how pilots reduced mapping time from 30 minutes to seconds, and how autonomous drones could transform wildfire monitoring.

SHOW NOTES

Episode Summary

Host Neal Spungen talks with Melvin Sieben about technology that helps first responders monitor wildfires in real time and predict where fires are most likely to start. Melvin discusses Mejor Technologies’ products, field results, and vision for collaborative wildfire intelligence.

About the Guest

Melvin Sieben is the CEO and founder of Mejor Technologies in the Netherlands. With a background in applied physics, he co-founded the company with mechatronics engineer Joris. Its products include CHRIS Cloud, the CHRIS Off-Grid Box, Lucy, and FAST.

Topics Covered

• CHRIS Cloud converts drone imagery into real-time fire maps, identifying fire boundaries, road access, and people who may need evacuation.
• The CHRIS Off-Grid Box provides mapping for five to six hours without external power or mobile coverage.
• Lucy uses region-specific machine learning models to predict wildfire susceptibility at 10 × 10-meter resolution up to 14 days ahead.
• How model accuracy is evaluated by examining fires occurring in the highest-risk areas.
• Plans for customizable parameters and model retraining.
• FAST’s vision for autonomous drone monitoring with multiple drones and automated battery swapping.
• Regulatory challenges surrounding autonomous and beyond-visual-line-of-sight flights in Europe.
• Connecting CHRIS and Lucy through vegetation data extracted from drone imagery.
• Pilot projects in the Netherlands and a demonstration in Bulgaria through the Fire Resilience Open Innovation Challenge.
• Field results showing geo-referenced images available in approximately 20 seconds, compared with up to 30 minutes for manual mapping.
• Potential applications in prescribed burns, industrial fires, port management, and missing-person searches.
• Collaboration with VU Amsterdam and a Dutch fire agency.
• Why open data exports and integration with existing dashboards are central to the company’s approach.

Key Takeaways

1. Live, shared maps help incident commanders make faster, better-informed decisions.
2. Drone thermal imagery helps identify fire boundaries and surrounding conditions.
3. Off-grid systems maintain critical mapping when power and mobile networks fail.
4. Region-specific machine learning improves wildfire susceptibility forecasting.
5. Regulations remain a major barrier to fully autonomous drone operations.
6. Collaboration and open data sharing can strengthen wildfire response.

Resources

Mejor Technologies: https://www.mejortechnologies.com/
Neal on Fire: https://nealonfire.com/

Daniel Doulton, CEO and co-founder of Solaris Suborbital, joins Neal on Fire to explain how autonomous, ultralight aircraft operating at 60,000–70,000 feet provide continuous video and sensor coverage over wildfire-prone areas. Discover how these stratospheric platforms bridge the gap between satellites and conventional aircraft, using thermal, radar, and hyperspectral sensors to improve wildfire detection, support first responders, and enable early suppression through the XPRIZE autonomous wildfire track.

SHOW NOTES

Episode Summary

Host Neal Spungen talks with Daniel Doulton about Solaris Suborbital’s autonomous, high-altitude aircraft and their potential to transform wildfire monitoring. Operating in the stratosphere, these reusable platforms can remain over target areas for months, providing continuous intelligence to support wildfire prevention, active incident response, and post-fire monitoring.

About the Guest

Daniel Doulton is the CEO and co-founder of Solaris Suborbital, a European company based in Spain that develops reusable, autonomous high-altitude flight platforms. With a background in AI, he recognized the potential of rich, continuous video data for improving wildfire intelligence and disaster response.

Topics Covered

• How Solaris aircraft operate at 60,000–70,000 feet, between conventional aircraft and satellites.
• The stratosphere’s “Goldilocks zone” and how persistent aerial coverage complements satellite imagery.
• Using continuous video to fill the gaps between satellite passes.
• Supporting all wildfire phases, from risk assessment and fuel monitoring to active response and post-fire analysis.
• How radar can detect conditions through smoke, clouds, and vegetation.
• Using mobile phone location data to help locate and alert people in danger.
• Aircraft endurance of two to three months and rotating fleets for continuous coverage.
• Coverage of approximately 3,000–7,000 square kilometers per aircraft.
• Swappable sensor payloads, satellite communications, and cloud-based data processing.
• Thermal, radar, and hyperspectral sensors for wildfire monitoring.
• The XPRIZE autonomous wildfire track, combining detection with drones for early-stage fire suppression.
• Observation-as-a-service business models and optional aircraft ownership.
• Collaborations with agencies such as NASA, the European Space Agency, and NOAA on climate and atmospheric science.
• Applications beyond wildfire, including flood monitoring, atmospheric research, and security.
• Regulatory coordination with the FAA and EASA.
• The economics of stratospheric aircraft compared with satellite-based observation.

Key Takeaways

1. Continuous aerial coverage fills critical time gaps between satellite observations.
2. Stratospheric platforms provide detailed, persistent monitoring over high-risk areas.
3. Radar can improve situational awareness through smoke and clouds, day or night.
4. Swappable sensors support wildfire monitoring, flood response, and scientific research.
5. Combining detection with autonomous suppression drones could improve early wildfire response in remote areas.
6. Solaris aims to deliver persistent monitoring with aircraft economics rather than space economics.

Resources

Solaris Suborbital: https://www.solaris-suborbital.space/
Neal on Fire: https://nealonfire.com/

Dr. Georgios Tzoumas, a swarm robotics researcher, joins Neal on Fire to explain how swarms of autonomous drones can detect wildfires early across massive landscapes. Learn how the dynamic space partition algorithm and an Archimedean spiral search pattern work, how a 30-drone simulation covering California detected nearly 88% of static fires, why swarms stay resilient when drones drop out, and what regulation, communications, and hardware mean for real-world deployment.

Show Notes

Episode summary

In this episode of Neal on Fire, host Neal Spungen talks with Dr. Georgios Tzoumas about swarm-based wildfire detection. He explains how a fleet of autonomous drones, controlled by one operator, can divide up a large landscape, search it efficiently, and keep working when some aircraft drop out. The conversation covers the algorithms, the user input from firefighters, the research results, and the practical barriers to real-world use.

About the guest

Dr. Georgios Tzoumas is a researcher developing swarm control software for autonomous drones in firefighting, including work with the University of Bristol and a consortium of partners. His team has worked directly with more than 50 firefighters and drone operators, along with fire and rescue services such as Lancashire Fire and Rescue, to shape the system.

Topics covered

* Why early detection is the top priority for firefighters, and what drove the research
* Misconceptions about drones: larger, long-endurance aircraft versus small quadcopters
* What a swarm is, how it takes inspiration from flocks of birds, and why it scales well
* Letting drones land to refuel or recharge without the fleet losing capability
* A plug-and-play, platform-agnostic software approach that works with different drone types and payloads
* Allowing one operator to control multiple aircraft, plus the user interface designed with firefighters
* The dynamic space partition (DSP) algorithm, which divides a search area based on the number of available drones
* What an Archimedean spiral is and why it works better than random walk or logarithmic spirals for search
* A simulation of 30 drones covering California over 24 hours, detecting almost 88% of static fires
* Simulations of more than 150 drones operating together
* Resilience: how the system still detects about 70% of fires when the swarm drops to around a third of its original size
* Limitations: hardware endurance and wind tolerance, communications weight and range, and beyond visual line of sight regulation
* Testing with civil aviation authorities and the FAA
* Work toward dynamic fire scenarios as part of the XPRIZE competition, where the team is a semifinalist
* Where drones fit alongside satellites, sensors, and cameras across prevention, active incident, and post-incident phases
* A realistic timeline: specific operations in three to four years, broader use in about six years
* An invitation for firefighters and agencies to explore pilot projects

Key takeaways

1. Swarms are scalable and resilient because drones follow simple local rules and the fleet reorganizes itself.
2. A single operator controlling multiple aircraft is a key goal, but regulation is still catching up.
3. Spiral search patterns provide organized coverage with consistent spacing, which matters for finding fires.
4. Real-world deployment will likely start with smaller areas and specific use cases, such as post-incident hotspot patrol.
5. Hardware, communications, and legal frameworks are bigger hurdles than the software.
6. Working closely with firefighters shaped the requirements.

Resources
https://nealonfire.com/

Theodore Giannaros, Senior Researcher at the National Observatory of Athens, joins Neal on Fire to explain fire weather and how extreme wildfires interact with the atmosphere to create their own weather. Learn what pyroconvection is, how pyrocumulus and pyrocumulonimbus clouds form, why the new ROSETTA project is building Europe’s first catalog of pyroconvective wildfires, and how earlier warnings could help firefighters prepare days in advance.

Show Notes

Episode summary
In this episode of Neal on Fire, host Neal Spungen talks with Theodore Giannaros, Senior Researcher at the National Observatory of Athens and fire weather expert, about how weather and wildfires influence each other. Theodore explains fire meteorology, how extreme fires couple with the atmosphere and generate their own weather, and how the new ROSETTA project aims to catalog and understand pyroconvective wildfires across Europe.

About the guest
Theodore Giannaros is a Senior Researcher at the National Observatory of Athens and a member of the FLAME research team, which runs fireweather.eu. The team began focusing on fire weather in 2017, growing out of a regional research project involving Greece, Bulgaria, and Cyprus, and has since worked to give weather and atmospheric science a larger role in European wildfire management.

Topics covered

* What fire meteorology is: how weather and climate influence wildfires, and how fires influence the atmosphere in return
* The origins of fireweather.eu and the FLAME project, and why weather has been treated as a static data input in Europe
* What the ROSETTA project is: a catalog of verified pyroconvective wildfire events across Europe, built with satellite imagery and other data
* Linking events to atmospheric conditions to identify thresholds for pyroconvection, then examining past and future trends
* Why a resource like this didn’t exist: a cultural gap between meteorology and wildfire management
* Lessons from the 2017 Portugal fires and the 2021 Greece wildfire season
* Why climate projections that rely only on surface-based indices may underestimate future fire danger
* The difference between “normal” wildfires and extreme events that couple with the atmosphere
* How heat from intense fuel burning creates rising air, circulation, and clouds such as pyrocumulus and pyrocumulonimbus
* Why modeling pyroconvection is hard, and the need for field observations using radar, radiosondes, and lidar
* Whether pyroconvective events are increasing, or just more visible because of social media
* Early warnings possible four to five days ahead for the 2021 Greek fires, and hopes for sub-seasonal outlooks about a month ahead
* Making research usable for firefighters under high cognitive load
* Collaboration with San Jose State University and Dr. Craig Clements, and the case for breaking down research silos
* Plans to extend the methodology to North America, Australia, and beyond
* A timeline: the catalog expected in about one and a half to two years, with a clearer picture by the end of 2029

Key takeaways

1. Extreme wildfires can create a coupled fire-atmosphere system in which the fire shapes the weather and the weather feeds the fire.
2. Surface weather indices alone may not capture the risk from pyroconvection.
3. Early warnings several days ahead may be possible by tracking how primed the atmosphere is.
4. Better field observations are needed to model and eventually predict pyroconvective events.
5. Close collaboration between researchers and practitioners is essential for tools to be useful in the field.
6. ROSETTA aims to answer whether pyroconvective wildfires in Europe are increasing, stable, or decreasing.

Resources
https://nealonfire.com/

J.K. Kinsey, founder of Ohana Goats, joins Neal on Fire to explain how goats and sheep reduce wildfire fuel loads in a natural, sustainable way. Learn how targeted grazing projects work, including electric net fencing, livestock guardian dogs, and herders, why goats and hair sheep are used together, how the team handled a wildfire mitigation project at the Grand Canyon, and why government agencies are a key target market for vegetation management with livestock.

Show Notes

Episode summary
In this episode of Neal on Fire, host Neal Spungen talks with J.K. Kinsey, founder of Ohana Goats, about a low-tech, high-impact approach to wildfire risk reduction: using goats to clear brush and other fuels. J.K. explains how a typical grazing project works, who is on the team, what goats will and won’t eat, and why the approach also benefits the land.

About the guest
J.K. Kinsey is the founder of Ohana Goats, based in Montana and serving the Western United States, including Arizona and California. The company has access to about 300 goats and focuses on wildfire fuel reduction in sensitive areas, along with invasive plant control and other vegetation management.

Topics covered

* What Ohana Goats does and how it began with a few goats for noxious weed control on a Montana ranch
* A recent wildfire mitigation project at the Grand Canyon, running from April through part of July
* What a typical project looks like: livestock, guardian dogs, electric net fencing, water tanks, and an on-site herder
* Why the team combines goats (browsers) with hair sheep (grazers) to clear brush and grasses
* What goats prefer, which plants they avoid, and how they tend to steer clear of poisonous plants
* How much ground the herd can cover, and why vegetation height changes the answer
* Logistics: moving about 300 goats by semi-trailer, setting up fencing, and monitoring the herd
* How herders are found and trained, and what a herder’s daily routine involves
* The roles of livestock guardian dogs and herding dogs
* How communities bond with the goats, plus funny stories from the field
* Ideal clients: city, state, and federal governments, as well as private landowners
* Explaining the benefits of grazing compared with mechanical equipment
* Regenerative benefits: added nutrients, soil aeration, and renewed plant growth
* Growth goal of reaching 1,000 head within about a year and a half
* The personal motivation behind reducing chemical herbicide use

Key takeaways
1. Goats and sheep graze different plants, so a mixed herd clears fuel more completely.
2. Grazing projects need more than goats: fencing, water, guardian dogs, and trained herders.
3. Projects vary with acreage and vegetation density, so there is no single “acres per day” figure.
4. Livestock grazing can add nutrients and aerate soil, giving benefits beyond fuel reduction.
5. Government contracts are a priority, and larger herds may be needed to support them.
6. Reducing chemical use is part of the mission behind the work.

Resources
https://ohanagoats.com/
https://nealonfire.com/