Colorado's forests are changing faster than the data about them.

The problem

The information landowners and foresters both need, from parcel boundaries and habitat to infestation, fuels and funding programs, lives across many agency websites and is split between university, state and federal sources. Keeping it current is hard: aerial surveys take time to reflect what a forester finds on the ground.

Infestations and wildfire cross property lines, but funding programs are built around individual parcels. A small parcel can be ineligible on its own while hundreds of surrounding acres face the same threat. With record drought, active budworm and beetle infestations, and shrinking federal support, quicker and more accurate data could help landowners understand their land, act with their neighbors and get a plan approved.

Hartsel, Park County. Field photos by Joel Heaton (reused from v1).

The flow

Earth Steward's flow comes from six years of studying field operations in oil and gas and in telecom fiber infrastructure. A field operator locates an asset, defines the impact, adds details and submits it to escalate. A landowner and a forester do the same work, on the ground and in geospatial terms. Only the context, assets, hierarchy and remediation change.

Who it's for

Landowners, foresters and TSPs (Technical Service Providers).

The narrative

A one-stop shop for analysis: fuels, EPA and crowdsourced data, with parcels stitched together, even small ones. Every place has land practices or concerns worth calling out, and Earth Steward pairs what people want with where they are: constraints, concerns, solutions and an interconnected source of truth. The product grows in value with more users and more content, through network externalities.¹

The pilot

For the pilot I chose my own parcel in Hartsel, Colorado. It's accessible, it has documented infestation concerns, and it needs a land stewardship plan to reach available funding, which makes it a realistic starting point.

I used Park County assessor data for the parcel and its boundary, and CSFS documentation to understand the real threats nearby, including Western Spruce Budworm and pine beetle. From there I built a working React prototype around the parcel as the pilot and testing environment.

Where AI fits

AI shortens the time between what's happening on the land and what landowners and foresters can see. It assists each step of the flow, and people make every decision.

Strategic role of AI (Delta Model)³

Hypothesis, How might we?

How might we give Colorado's private landowners one place to research, apply for, document, carry out and fund forest stewardship? As stewardship creates records on private land, those records can connect with neighbors' projects and with state, national forest and BLM land, linking water, soil, wildlife corridors, infestation data and farmland into one living map of the landscape. Joel Heaton

How results will be measured

Parcel submissions first, then plans submitted and approved, grant applications, acres enrolled, connected neighboring parcels, and time saved per landowner.

Foundations

What I brought into the research. I took MIT xPRO's course to apply AI to something I'm passionate about, and to use Earth Steward as a case study for continuing my career in conservation, GIS, AI and operations.

Document and tech reviews

MIT xPRO workbooks: the four-stage AI design process (Module 1), a GAN-based driveway study on the pilot parcel (Module 8), and the capstone with its double diamond (Module 9).

V1: what the first build taught

Version 1 was my MIT xPRO capstone, built with the AI design process rather than this Mixed Methods cycle, so here it counts as research: something built, studied and learned from. It included the first case study, a Figma design library based on Shadcn UI, the ES V1 Mobile 1.0 Figma prototypes, a React prototype and an HTML desktop parcel map.

What people said

People who viewed V1 valued:

• Understanding anomaly detection.
• Progressive disclosure: information revealed step by step, not all at once.
• Seeing their parcel detected, with its boundary and habitat zone, and what trees to expect there.
• The help the app provides along the way.
• Being told about nearby infestations and the likelihood their trees are affected.
• Resources, treatments and agencies in one place.
• Being guided through applying for funding.

Foresters raised one concern: landowners might treat the app's output as final, make a plan and submit it without confirmation. That's a key finding. A forester in the loop is essential, so V2 has to make the forester's review a required step, not an optional one.

Those gaps set the scope for V2: a reworked design library, new Figma prototypes and a React app that is truly mobile, tested on iPhone.

Survey

A five-minute survey sent to landowners, foresters, TSPs and conservationists about technology in conservation and forest management planning.

Questions

Needed Survey questions to add.

Synthesis

First response in: a forester, anonymous. Synthesis starts at five or more responses, for a cross section, and collection continues after that.

Interviews

An initial round, each bringing a different lens: woodland ownership, wildlife management, watershed and fire science, and consulting forestry.

• Founder, national woodland owners associationFormer U.S. Forest Service forester. Took the part of a landowner. At first taken aback by AI, then saw value in using data wisely; his hesitation is the tech side, a useful signal for designing for older landowners.
• Retired federal wildlife chiefUSGS and USFWS Northeast. Wrote in with what he saw as positive about Earth Steward.
• State watershed scientistBackground in forestry and fire research in the Northern Rockies. Surprised how closely his work overlaps with Earth Steward.
• Massachusetts consulting foresterIntroduced Gaia AI's backpack LiDAR system, which maps terrain as the forester walks and takes observations by voice. Notes to come.

Questions

Needed Interview questions to add.

Synthesis

Needed Interview synthesis to come.

User flow

Flow V2 builds on the hypothesis and on years of studying field operations. The landowner opens Earth Steward and locates the parcel from the survey or assessor map, or from drone images, with SAM 3 working on whichever comes in. Risk is read from elevation and habitat class, inventory, the probability of cross-boundary infestation, and fuels. The forester adds the ground inventory, then the plan moves through the FMP 106, funding, contracts, approvals and the work itself.

Personas

First drafts, to be refined with survey results.

Joel and Kristen

Landowners, late 50s

About 12 acres in Park County, bought as an investment and a place to enjoy. Active infestation on and around the land. They camp there now and will build a small second home someday.

Goals

See what's on the land, learn its species, improve its health and value, reduce fire risk, apply for funding, and understand how the parcel connects to nearby BLM land and wildlife.

Frustrations

Information spread across a dozen agency sites, aerial maps that lag what they see, only some weekends on the land, and a parcel too small to qualify alone.

Earth Steward helps by

Turning a weekend visit into a documented parcel, a habitat zone and a path to a forester and funding, with the parcel shown in context.

"We want to take care of this land and understand how it connects to everything around it, but we don't know where to start or who to ask."

Chris

USDA/NRCS forester, early 50s

Grew up on a ranch, earned a forestry degree from Purdue, and moved to Colorado 25 years ago. Knowledgeable, thorough and empathetic; licensed in NRCS practices.

Goals

Help as many landowners as he can, write sound plans, and get treatments funded and done.

Frustrations

More landowners than site visits, landowners who arrive with little information, aerial data that lags his findings, and paperwork that takes time from the woods.

Earth Steward helps by

Handing him a parcel that's already located, classified and photographed, ready for his ground inventory and sign-off.

"If they came to me knowing what's on their land, I could spend my time solving problems instead of finding them."

Jebbo

Technical Service Provider, 40s

Ten years in Colorado, a marketing background, and a love of skiing, biking, hiking and kayaking. Became a TSP to earn a living writing FMPs and to spend more time outside.

Goals

Build a steady client base, write FMPs efficiently and correctly, and work outdoors.

Frustrations

Newer to forestry than the foresters around him, so he leans on good data and clear formats. Finding clients takes time.

Earth Steward helps by

Bringing him landowners ready for a plan, with inventory data already structured for the FMP.

"I want to help landowners and be in the field, not chase data across ten websites."

Forest Management Plans

Foresters write Forest Management Plans through NRCS in two parts. The CPA 106 plan sets the strategy: goals, inventory, diagnosis and prescription. The DIA 165 design turns it into specifications: which trees, size, species, spacing and health. FMP writing is the step Earth Steward's Submit hands off to.

To learn the process firsthand, I found a point of contact at NRCS and started Technical Service Provider certification. With a forester as my sponsor, I'm working through Colorado's USDA/NRCS requirements and will submit two FMPs. Once certified, I may write plans around Hartsel, drawing on what I know of its habitat and practices, and connect my own parcel with surrounding parcels to test the crowdsourcing and network-externality idea of stitching land together.

V1's feedback shaped the direction for V2. A landowner can start the process (Locate and Impact), but soon after, a forester or TSP needs to step in to approve it, guide the inventory and documentation, and walk the plan through NRCS's Nine Steps of Conservation Planning.

That's why I'm pursuing TSP certification. Unless I understand this process fully, I can't assume the app will work. Somewhere in that training I may see a solution that connects field operations with government and conservation initiatives.

NRCS criteria (October 2024): CPA 106, Forest Management Plan (PDF) and DIA 165, Forest Management Practice Design (PDF).

Habitat zone classification

Earth Steward takes the parcel boundary from Park County assessor data and overlays it on a USGS elevation model to get the parcel's elevation range. Elevation, aspect, slope and precipitation are matched to CNHP's six habitat categories with a Random Forest model trained on that labeled zone data. The result assigns a habitat zone with its typical species, threats and practices. working

The desktop parcel map was the first proof of concept, before the mobile app. The zone was classified as Montane, and the tree species at that elevation were called out: ponderosa pine, Douglas-fir, lodgepole pine, aspen and blue spruce. That carried through to the landowner's parcel page in the React prototype.

Published life-zone ranges disagree, so the source is named: the Colorado Virtual Library and CSU's Colorado Natural Heritage Program.

Map 3: desktop parcel map, the first proof of concept

GIS and SAM 3

GIS brings together Park County parcel polygons and adjacent boundaries, elevation, NAIP aerial imagery, Gaia GPS tracks and boundaries, and the CSFS aerial survey. Meta SAM 3 segments the canopy from aerial or drone imagery and, on the ground, finds damage in photos from a text prompt. Paired with a model trained in Roboflow, it would detect anomalies, identify species and flag crown infestations. concept

See the parcel viewer and Gaia GPS maps in Maps and documents.

Drones and LiDAR

No single source is fully accurate, so data fusion is the core job. In the MIT driveway study, I designed an access road for the parcel from a survey plat, a route drawn in Gaia GPS, geotagged photos and county elevation data. Every source disagreed with the others somewhere, so the real work was reconciling imperfect inputs into one terrain model good enough for engineering decisions.

Professional platforms already do this at scale. Caterpillar and Trimble turn terrain data into buildable designs, but they're built for contractors and large projects, not a landowner with 12 acres and a phone.

LiDAR sees through the canopy to bare earth, which a driveway needs, and it measures the canopy itself, which a forester and a fuels model need. Drone imagery adds detail and timing that aerial surveys can't match. The same capture that plans a road can inventory a forest.

Gaia AI, an MIT spinout, pairs a backpack LiDAR with a mobile app. Foresters walk a cruise path while it measures height, diameter, live crown, species and density and builds a 3D point cloud. It fuses satellite, drone, below-canopy LiDAR, computer vision and forester input, the same thesis as Earth Steward. (Gaia AI is unrelated to Gaia GPS.)

Sensing layers

• SatelliteSees everything, late.
• DroneSees the canopy.
• Backpack LiDARSees under it.
• Phone photoSees one tree, right now.

Each layer is more precise and less scalable than the one above it. Earth Steward fuses them, starting with the layer every landowner already carries, and hands off to a forester who can bring a drone or backpack for the inventory an FMP needs.

Caterpillar shows where this is heading at industrial scale: autonomous machines working from a constantly updated, 360-degree view built from LiDAR, radar, GPS and cameras, with people validating the decisions. Its CES 2026 keynote lays out that vision. Earth Steward applies the same principles, fusion, digital twins and a human in the loop, to a landowner with 12 acres.

What this research is really about

Backpack LiDAR, Caterpillar's autonomous jobsites and the GAN driveway study all point the same way: tools once reserved for large projects can now help a private landowner plan with real awareness of terrain. Researching them is how I find out what's affordable and what's within reach. That matters because funding for landowners and for state and federal conservation programs has been cut, so the right tool can be the difference between a plan that happens and one that doesn't. Many landowners also don't know what's already available to them. Cheaper drones keep arriving, government data is public, and processing power is more available than it has ever been. As funding shifts, stewardship may lean more on private and nonprofit partners like the National Forest Foundation and The Nature Conservancy, and on tech firms built around sustainable practice. Earth Steward is designed to work whichever way that goes: matching each landowner to the most affordable source that answers their question.

Data capture options

In the Locate step, the landowner picks a source. These run roughly from free and broad to costly and precise. Earth Steward starts each landowner on the cheapest rung that answers their question and climbs only when the plan or funding needs more precision.

Data sources

See Map 4 (CSFS), Map 7 (DWR) and Map 11 (Wild Horse Reservoir) in Maps and documents.

Gaia GPS and onX

Gaia GPS has proven the most useful in my own fieldwork. I pay for it and use it for tracks, areas, points of interest, user-generated content, parcels, and MVUM, OHV, USFS and satellite layers, with tracks going back to 2023. onX likely offers more for private-land ownership, which is its core. I chose Gaia GPS originally for NOAA nautical charts for boating in Virginia; when NOAA retired that chart service in 2022, Gaia GPS swapped in a less detailed version. That's a real-world example of the data-reliability problem Earth Steward addresses.

See Maps 8–10 in Maps and documents.

Forest fuels

CO-WRAP (Colorado Wildfire Risk Assessment Portal) is the statewide layer for long-term risk. Watch Duty maps active fires and prescribed burns in real time and is a strong mapping and UX reference. The science behind fuels comes through a watershed scientist who spent about eight years at the USFS Missoula Fire Sciences Lab. His point for Earth Steward: fuel loading has signatures that link to satellite and raster imagery, which is what makes an AI fuels layer possible.

Maps and documents

Each piece shows my own use, study and research, on the ground and geospatially. Neighbors are labeled by direction and acreage, never by name.

Parcel viewer

Live: Park County parcel and contour data for the pilot parcel and its ten neighbors, on Esri World Imagery. Contours every 20 ft, 8,880–9,340 ft. Owner names and addresses removed; neighbors shown by direction and acreage. This stands in for the survey plats.

Map 4: CSFS Aerial Detection Survey 2025