How Artificial Intelligence is Reshaping Forest Management

By Jennifer Hunt (Content Writer) and Ross Wygmans (GIS Analyst)

As Artificial Intelligence (AI) becomes a daily part of life, we see how it makes its presence known through everything from art creation to automating workflows and analyzing data. It’s also finding its place in forestry, helping to automate processing, improve accuracy, and reduce costs.

In this issue of Root to Canopy, we explore how AI, GeoAI, and modern digital tools are shaping forestry and land management by streamlining data, supporting better decisions, and modernizing everyday workflows.

Standardizing Data

In the past, client data had been stored in separate databases, each prized for its own nuances. Unsurprisingly, this led to overlapping datasets and mismatched map projections, causing fragmented data. As a result, even a simple regional analysis turned into a juggling act.

“It was a bit like having ten musicians all playing the same song in different keys,” one AFM GIS Analyst shared. “Each dataset sounded fine on its own, but together it was anything but harmonious.” By moving to a centralized database, AFM has brought those datasets in tune. The new system streamlines workflows, improves data consistency, and makes collaboration across client properties a much smoother process.

This standardization directly supports the mobile apps foresters use in the field, like ArcGIS Field Maps. Rather than juggling a dozen separate layers for roads, stands, and streams, foresters now see a single layer for each, allowing for faster loading of maps and reduced clutter. Foresters can tap directly on a stand to see its full set of attributes, including volumes, stand history, and notes. This interactive access to live data adds a new level of usability and insight that paper maps or static PDFs simply cannot match. The map also includes all of our core corporate datasets, including ownership, stands, roads, streams, and gates, along with elevation contours and tools for laying out timber sales, reporting invasive species, and identifying road issues, all within a single app.

Additionally, the rise of digital tools has allowed us to transition from paper-based forms, further streamlining data. In the Pacific Northwest, we digitized our inspection forms in Survey123, which proved to be especially beneficial during our Sustainable Forestry Initiative® (SFI®) audits. Not only has this change streamlined data collection and reporting, but it has also reduced the opportunity for human error when transferring information from paper forms to spreadsheets. These survey results feed directly into ArcGIS Online, allowing us to build dashboards and apps that summarize and visualize inspection results more intuitively. With Survey123’s Power Automate connectors, we can even trigger automatic report generation and email notifications once a project is finished.

Simplifying and Accelerating Data with AI

AFM uses Python, an object-oriented language widely used in GIS and data automation. AI tools like ChatGPT and AI coding assistants such as Cursor AI have transformed how analysts develop scripts for streamlining GIS workflows. Additionally, these tools help analysts build and refine Python scripts for tasks such as schema comparisons, data validation, and batch updates across multiple feature classes. Coupling these tools with Jupyter notebooks has changed how analysts document their work. “Jupyter is kind of like a Python recipe book that breaks a workflow into clear, step-by-step ingredients, where code, notes, and results all live together. It makes my scripts easier to share, review, and reuse across projects,” a GIS Analyst shared.

Scripts that may have previously taken weeks or even months to cobble together can often be built in an afternoon, freeing analysts up to focus on the bigger picture: designing smarter workflows, improving data quality, and finding new ways to make our GIS systems more efficient.

Balancing Automation with Accuracy

AI tools offer significant benefits, but using them is not without risk. Although a user can prompt it to generate a script, that doesn’t guarantee accuracy or quality. Using AI tools in tandem with a program like Jupyter notebooks allows the user to move step by step, review outputs along the way, and validate that each part of the process is behaving as expected. It is essential to have a solid foundation in programming to ensure that errors are caught before they touch production data. While AI can speed processes up dramatically, it does not replace careful quality control and human reasoning.

The Rise of GeoAI in Forestry

GeoAI, the integration of AI with geospatial analysis, is opening exciting new doors for forestry. While machine learning has long been used to classify imagery or detect change, modern AI tools can process far larger datasets and uncover complex spatial patterns in ways that were once impossible.

Over the next five years, some expect GeoAI to play a major role in:

• Automating stand delineation from imagery and LiDAR.
• Enhancing forest health and volume estimates through early detection of stress or mortality.
• Integrating analysis directly into planning workflows for faster, data-informed decisions.

AI-driven models can scan drone or satellite imagery to flag problem areas, delineate stands automatically, and even suggest more efficient harvest layouts. What once took hours of manual editing can now happen in minutes, freeing foresters to focus on strategy instead of data cleanup.

A Glimpse Into the Future

Perhaps one of the most exciting frontiers is the creation of a “digital twin” of the forest, which is a living, data-rich 3D model powered by AI, LiDAR, and drone imagery.

Using backpack LiDAR systems and autonomous sub-canopy drones, foresters can now capture every tree and canopy structure with incredible detail. AI models then process the data to identify species, measure diameters, and map forest health.

While cost and complexity still limit widespread use, advances in AI are making these technologies far more accessible. Techniques like transfer learning allow generalized models to be fine-tuned to local conditions using relatively small datasets. This breakthrough could make high-resolution forest inventories practical for a much wider range of properties.

AI has significant potential to make forest monitoring and planning faster and more responsive. It can automatically delineate stand boundaries from imagery and LiDAR, detect small differences in species composition, canopy structure, or age class. With harvest planning, it can integrate terrain, access, and stand data to suggest more efficient layouts or highlight suspected problem areas.

The journey from fragmented data to integrated intelligence is more than just a technical upgrade, but a shift toward faster, smarter, and more transparent forestry practices. As AFM continues modernizing its tools and workflows, these advancements are creating a future where data and decision-making move seamlessly together, supporting better planning, clearer insights, and more agile operations. Together, these tools position AFM at the forefront of a rapidly evolving industry, helping shape a more modern, sustainable, and efficient approach to forest management.