What the Trees Are Worth
TALON started as a parcel scoring platform. Boundary data, soil classifications, slope analysis, proximity to roads and utilities — the kind of information that tells you whether a piece of land is worth looking at. Useful. But I kept running into the same wall: for rural wooded parcels, the most important question is usually the one nobody can answer without boots on the ground.
What’s the timber worth?
I grew up around that question. My stepfather is a forester and logger, and I spent a good chunk of my adolescence and early adulthood working with him — walking boundary lines, assisting on timber sales, running a chainsaw, driving a skidder. I know what a mature hardwood stand looks like and roughly what it’s worth. I know the difference between a merchantable oak and a wolf tree. I know that stumpage value varies wildly by species, access, and slope.
What I didn’t have was a way to answer that question at scale, across thousands of parcels, without walking every one of them. That’s what pulled me into 3DEP data.
First Contact with the Point Cloud
My initial goal was simple: get a canopy height estimate from publicly available data. 3DEP LiDAR seemed like the obvious starting point — USGS has been flying the entire continental US for years, the data is free, and it covers the rural parcels TALON is built for.
What I didn’t fully appreciate at the start was how much information is actually in a point cloud. I was thinking of it as elevation data with some tree height on top. That’s technically true and also completely misses the point.
A LiDAR point cloud is a three-dimensional record of every surface the laser hit — ground, understory, mid-canopy, upper canopy, bare branches, dense foliage. The vertical structure of a forest is in there. Not inferred from satellite indices or estimated from county averages. Actually measured, return by return, across every acre of the parcel.
The first time I rendered a parcel’s full point cloud in 3D and started rotating through it, something clicked. I’d walked hundreds of forested parcels over the years and developed an intuitive read for a stand’s character — its density, its age class, its species mix — from what you see at eye level walking through it. Here was something different: the same information, but from above, at the resolution of a laser pulse, covering the whole property at once. It wasn’t elevation data. It was the forest itself.
Building Toward Timber Value
Getting from “point cloud exists” to “timber estimate” required building a lot of infrastructure I didn’t anticipate.
The pipeline starts with separating ground from non-ground returns. Once you have a bare-earth surface, you can compute canopy height at any location by subtracting ground elevation from the highest return above it. Do that across the whole parcel and you have a canopy height model — a spatial map of how tall the trees are, at one-meter resolution.
From canopy height you can start estimating timber volume. Height is the primary driver you can reliably extract from 3DEP data at parcel scale, so I built multiple valuation tiers based on different canopy height thresholds and regional stumpage pricing — giving buyers a range rather than false precision. A parcel with 80-foot canopy in a county with good hardwood markets is a different conversation than the same height in softwood country. The platform accounts for that.
What surprised me was how much the point cloud revealed beyond simple height. Stand density, canopy gaps that suggest past selective harvest, the difference between even-aged plantation structure and uneven-aged natural stands — things I’d previously only been able to read by walking a property, now visible in the data from a browser window.
The Viewer Problem
Early in this work I had a rough Three.js visualization that got the job done for internal testing but wasn’t something you’d want to hand to a buyer. It rendered terrain. That was about it.
As I got deeper into what the data actually contained, the gap between what was possible and what I was showing became increasingly frustrating. The full point cloud — canopy structure, ground surface, vertical forest layers — tells the story of a piece of land in a way that a topographic map never could. Someone who’s spent time in the woods knows immediately what they’re looking at when they see a properly rendered stand. Dense mid-story with tall dominant canopy means something different than an open understory with a uniform crown. That’s readable in the data.
I rebuilt the viewer. Colored by elevation, by height above ground, by classification layer. Controls that let you isolate just the canopy, just the ground, just the understory. The ability to orbit through the full 3D structure of a parcel and understand its topography and forest character in minutes.
That viewer is now central to what differentiates TALON. It’s not a map with a polygon on it. It’s the land.
What’s There When You Look
The practical result is that TALON can now surface timber value estimates for wooded parcels without a site visit — multiple tiers, regionally calibrated, derived from the same 3DEP data that underlies the rest of the platform.
But the bigger shift is in what I now know the data contains. I started this to answer one question and found that the point cloud holds a lot more than canopy height. Terrain features. Historical disturbance. The shape of a drainage. The signature of an old road.
I’m still figuring out how much of that is readable at the resolution public 3DEP provides. So far the answer keeps coming back: more than you’d expect.