FAC or Fiction: The Truth About Vegetation Risk and Transmission Resilience
For decades, traditional vegetation management for transmission operators has followed a structured approach: fixed-cycle trimming with manual inspections to identify critical issues and monitor contractor performance. These methods, shaped by NERC FAC-003 standards and lessons from past outages and wildfires, have aimed to ensure compliance and grid reliability.
And the landscape has changed.
Weather patterns have been more extreme while regulations are tightening and operational costs are rising. Fixed schedules and traditional inspections no longer reflect today’s risks. Drought conditions increase wildfire hazards, while severe storms bring new vegetation threats.
Advanced technology in the vegetation management space has also evolved. Getting up-to-date situational awareness used to require helicopter flights or expensive, risky field labor to identify, classify, and report on risks. Now, data-driven analytics allow for objective risk assessment. Vegetation intelligence complements traditional tools, helping operators prioritize critical areas.
Like any technology, remote sensing and AI have come with their own misconceptions. Here are some of the most persistent myths about vegetation intelligence for transmission operators—and the facts that set the record straight.
Myth #1: Fixed cycle maintenance is the best way to ensure compliance with regulatory requirements
| Concern | Reality | Practical Example |
|---|---|---|
| Fixed-cycle maintenance has been the standard for decades, and regulators expect a documented, predictable maintenance plan that other methods can’t offer. | Fixed schedules don’t consider varying vegetation growth rates across transmission corridors. A data-driven, risk-based approach provides even stronger documentation, showing targeted risk mitigation that accounts for variations in environment, tree health, and species instead of broad, uniform trimming. Regulations require effective risk mitigation, but they don’t mandate trimming every area on a fixed schedule. A risk-based approach still meets compliance while optimizing resources. |
Sho-Me Power improved compliance by prioritizing high-risk areas first, reducing unnecessary trimming while demonstrating proactive risk mitigation.
ATC transitioned from a fixed trimming schedule to a system-wide risk assessment model, improving both efficiency and regulatory compliance. |
Myth #2: Every dollar spent on data is a dollar not spent on clearing the ROW
| Concern | Reality | Practical Example |
|---|---|---|
| Investing in additional data tools takes away from actual vegetation management, especially when budgets may already be stretched. | Spending on better data doesn’t replace trimming—it makes it more efficient by optimizing contractor work, ultimately demonstrating greater ROI.
Proactive risk assessment reduces emergency response costs, regulatory fines, and unnecessary maintenance, freeing up budgets in critical areas. |
OG&E identified 80 critical spans in 30 minutes—work that previously took two days—allowing for faster, more cost-effective maintenance.
Sho-Me Power reduced unnecessary trimming by focusing on only 7% of its network that required urgent work, improving efficiency without increasing costs. |
Myth #3: LiDAR provides more precise data, so it should offer more actionable insights
| Concern | Reality | Practical Example |
|---|---|---|
| LiDAR provides precise measurements of vegetation clearance down to the centimeter, so teams may not want to add another tool to the toolbox. | LiDAR is highly accurate, but that level of precision isn’t necessary for most vegetation management decisions and it often requires expensive, time-consuming processing. The key isn’t just measuring vegetation but knowing where and when to act.
Satellite scans can offer more timely, affordable system-wide intelligence with clear prioritization of vegetation management work that doesn’t require cm-level data. Using these tools together to complement one another is often less expensive and more useful for teams. |
ATC combined LiDAR data with Overstory’s vegetation intelligence to develop a risk assessment model, improving efficiency and reducing unnecessary trimming. |
Myth #4: Vegetation intelligence is too complex to integrate seamlessly with existing workflows
| Concern | Reality | Practical Example |
|---|---|---|
| Scaling vegetation intelligence across thousands of miles of transmission corridors requires seamless integration with GIS and other asset management systems. We already have multiple platforms and don’t have the resources or desire to disrupt workflows. | Overstory’s vegetation intelligence is designed to integrate with existing work order and asset management systems. You can use Overstory data wherever you work and alongside existing GIS and LiDAR data seamlessly, reducing complexity rather than adding it. In addition, regulatory compliance is strengthened with more automated reporting. | Sho-Me Power integrated Overstory’s vegetation intelligence without disrupting existing contractor workflows, making side-trimming efforts more efficient. |
How are real transmission operators using vegetation intelligence today?
Managing vegetation across thousands of miles of transmission corridors requires balancing regulatory compliance, operational costs, and risk reduction. Traditional methods like fixed-cycle trimming, aerial patrols, and manual inspections have long been the foundation of vegetation management.
But new challenges are making it harder to maintain reliability while keeping costs under control:
Contractor bids are based on outdated estimates, driving up costs
Some spans are over-trimmed, while high-risk areas are missed
Verifying contractor work is time-consuming and inefficient
Maintenance cycles don’t always match real-world vegetation growth
More transmission operators are using vegetation intelligence to improve efficiency and resource allocation—enhancing existing methods to prioritize risk, optimize spending, and improve reliability. Here are a few relevant examples:
Optimizing Contractor Bidding
Outdated or generalized estimates in contractor bids often result in inflated costs or misallocated budgets. Crews may be assigned work in areas where they're not needed, while high-risk spans receive less attention than required. Without precise data, operators can risk overspending.
Before partnering with Overstory, Sho-Me Power used a time and materials approach to contractor bidding. Crews were sometimes deployed in areas that didn’t need urgent trimming, while other high-growth regions required more resources than initially estimated.
How does vegetation intelligence help? It provides data reflecting actual maintenance needs rather than rough estimates.
Prove it. Sho-Me Power moved from relying on historical estimates to satellite-powered vegetation data. This improvement in precision led to a 13x return on investment while cutting unnecessary maintenance costs.
Instead of bidding based on time and materials estimates, Sho-Me Power used vegetation intelligence to shift to data-driven lump sum bidding, streamline contract negotiations, reduce unnecessary trimming and mobilize crews more effectively.
Estimating Workload with Work Type Analysis
Not all vegetation requires the same approach. Traditional maintenance cycles apply a one-size-fits-all approach which can lead to redundant work and make budgeting for mowing, herbicide, and side trim projects particularly complex.
ATC initially split their network into different geographies, trimming vegetation trimmed at set intervals based on location rather than actual risk. This meant some spans were maintained too frequently, while others were at risk between scheduled trims.
By incorporating vegetation intelligence, ATC could define the specific work type needed—whether mowing, herbicide, or side trimming—based on vegetation type and proximity to conductors. This also helped contractors build more accurate bids.
If you’ve ever built a house or renovated a bathroom, you understand how scoping a project thoroughly and accurately ensures alignment around budgeting and timeline.
When contractors know exactly what work type is required, they can bring the right crew and equipment without needing to guess or visit the site, reducing travel time and cutting out the padding typically added to account for uncertainty.
How does vegetation intelligence help? It identifies specific work type necessary (herbicide, mowing, side trim, etc.) based on vegetation type and proximity to conductor, improving efficiency and cutting costs.
Prove it. ATC uses work type analysis in their program to make more effective decisions around where they send different crews and what types of equipment are necessary to mitigate critical risk around their assets. This reduces waste and ensures high-risk areas are addressed quickly.
Adopting a risk-based approach with the integration of vegetation intelligence, ATC could prioritize the right type of maintenance based on vegetation conditions and make smarter decisions about budgeting and crew deployment to mitigate critical risk faster and more affordably.
QA/QCing Contractor Work
Verifying contractor work is essential for transmission operators, especially those managing high-voltage transmission lines where compliance failures can lead to serious consequences. For ATC, where 20% of the system operates at over 200kV, vegetation management is critical to ensuring FAC-003 compliance and grid reliability.
Historically, confirming whether contractors trimmed the proper spans and met safety standards required manual ground patrols, helicopter flyovers, and periodic LiDAR scans. While effective, this approach was resource-intensive and expensive, leaving room for delayed issue detection and costly rework.
How does vegetation intelligence help?
It enables 100% QA/QC verification after vegetation work is completed, reducing reliance on manual inspections.
It supports compliance with FAC-003 by providing documentation of vegetation conditions before and after contractor work.
Prove it.
ATC implemented 100% QA/QC verification using Overstory’s vegetation intelligence, reducing unnecessary site visits while ensuring compliance.
Overstory’s insights provide continuous compliance tracking which allows ATC to reduce the administrative burden of FAC-003 reporting.
Before integrating vegetation intelligence, ATC relied on traditional QA/QC inspections that required:
Helicopter patrols to visually inspect spans post-trim
Ground crews manually verifying whether contracted trimming met standards
Expensive annual LiDAR flights to ensure encroachment risks were mitigated
While these methods provided important oversight, they were slow, expensive, and reactive.
To improve efficiency and maintain compliance without unnecessary costs, ATC adopted satellite-based QA/QC verification, allowing them to:
Verify completed work remotely: Instead of manual spot-checking, ATC can confirm whether every span has been trimmed according to contract requirements
Reduce costly LiDAR scans: Instead of annual LiDAR flights, ATC now uses a more balanced approach, integrating LiDAR only during mid-cycle inspections to reduce expenses
Enhance compliance tracking: Vegetation intelligence automatically documents pre- and post-trim conditions, ensuring that ATC stays ahead of FAC-003 reporting requirements
Prioritizing Hot Spot Trimming
Fixed-cycle trimming often allocates crews to low-risk areas while urgent spans go unnoticed. This reactive approach means that problem areas aren’t addressed until vegetation has already become a risk.
Previously, Sho-Me Power followed a traditional cycle-based trimming approach, which meant that some fast-growing spans became a risk before their next scheduled maintenance. By using satellite data and data-driven vegetation insights, they were able to identify only the highest-risk areas and prioritize trimming accordingly.
How does vegetation intelligence help? It pinpoints hot spots and hazard trees so crews can prioritize urgent areas first.
Prove it. Sho-Me Power used satellite-based analysis to prioritize 0.8% of spans for emergency hot spot trimming, shifting from two year round crews to eliminating all critical risks with one crew in just months. Holland at Sho-Me Power elaborated, "every single hot spot that Overstory recommended we trim has needed work," proving the accuracy of data-driven prioritization.
By directing crews to high-risk spans first, Sho-Me Power prevented outages and reduced emergency response costs while optimizing its maintenance budget.
Improving Cycle Plans Beyond Fixed Schedules
Traditional trimming cycles follow rigid schedules, even if some spans don’t need trimming yet. This results in wasted resources in low-growth areas and insufficient maintenance in high-growth regions.
Before implementing vegetation intelligence, OG&E relied on a four-year trimming cycle, applying the same schedule to all spans regardless of growth rate. As a result, some areas were trimmed unnecessarily, while others that required intervention sooner were missed.
How does vegetation intelligence help? It tracks real-world vegetation growth trends, allowing teams to adjust trimming cycles based on actual need rather than fixed schedules.
Prove it. OG&E optimized its cycle plan. They extended some substations from a four-year to a five- or six-year cycle. This has freed up resources for higher-risk spans. Steven Frazier, a vegetation management professional at OG&E, shared, "what we learned is that we no longer have to be reactionary. We can start being more proactive and prevent some of these problems before they ever happen."
By analyzing real-world vegetation growth, OG&E was able to adjust cycle plans dynamically—extending low-risk areas to a five- or six-year cycle without increasing risk while redirecting resources to fast-growing problem areas.
From Reactive to Proactive Vegetation Management
Instead of trimming on a fixed schedule or relying on historical estimates, operators are using vegetation intelligence to:
Ensure resources are allocated where they are needed
Select the right maintenance approach for each span
Meet compliance standards
Prioritize high-risk spans before they get escalated
Adjust schedules based on real growth patterns
Some resources to explore this shift toward proactive vegetation management further:
﹢The Playbook for Operationalizing Advanced Vegetation Intelligence: A guide to how transmission operators are integrating risk-based decision-making into their workflows
﹢The Guide to Evaluating Vegetation Intelligence Technology - A breakdown of important factors to consider when selecting a vegetation intelligence tool, from data accuracy to workflow integration
Where does vegetation intelligence fit into your strategy?
