NRMTech Silvibot Research About
39° 42′ 49″ N, 105° 07′ 08″ W / AI & Data Platform Manager · U.S. Department of the Interior

Martin Edward Hensley

Cities manage thousands of trees —
every inventory is outdated before it's done.
Building SilviBot — the first product in NRMTech.

Discover NRMTech Meet SilviBot Explore Research
01 The Category

NRMTech

AgTech has a name. FinTech has a name. HealthTech has a name.
Natural Resource Management Technology doesn't — yet.

The Problem

Urban forestry, operational forestry, utility vegetation management, water, wildlife, minerals — every domain of natural resource management faces the same technology adoption gap. 19,000+ municipalities manage tree canopy with diameter tapes, manual-entry handhelds, and windshield surveys. The work is fragmented across agencies, disciplines, and industries. Nobody has named the category, so nobody is building for it intentionally.

The Thesis

NRMTech is the technology category encompassing all tools, platforms, and systems built for natural resource management. Like AgTech created an ecosystem of companies, conferences, and career paths for agriculture — NRMTech does the same for the stewardship of public and private lands, waters, and resources.

The Landscape

Urban tree platforms (ArboStar, TreeKeeper, i-Tree), precision forestry, smart forestry, AI in conservation — all narrow subsets of the same unnamed category. Management software accepts data but doesn't collect it. The collection gap is unsolved. A $50B+ combined market across utility vegetation management, municipal forestry, and operational forestry — without a shared identity to accelerate the field.

02 Autonomous Tree Inventory

SilviBot

An autonomous ground-and-air sensing platform that inventories, measures, and monitors every tree in a municipality — continuously, verifiably, at a fraction of the cost of manual assessment.

$50B+Urban/Utility Market
19,000U.S. Municipalities
<2cmDBH Precision

The Problem

Cities manage trees as infrastructure but inventory them like it's 1999. The typical municipal arborist is responsible for 10,000 or more street trees with tools that amount to a handheld, a diameter tape, and a truck. Condition assessments are subjective, cycle times stretch to 5–10 years, and when a mature oak drops a limb on a parked car, the city's liability defense is "we inspected it... sometime."

Utilities spend $28–30B annually on vegetation management around power lines — driven by wildfire liability after PG&E nearly collapsed from a single tree-to-conductor contact. The data collection underneath all of this spending is manual, inconsistent, and unverifiable.

The Solution

SilviBot combines ground-level LiDAR with aerial sensing to produce a complete digital twin of every tree in its jurisdiction. A quadruped robot walks the streetscape scanning trunks — DBH, lean, basal cavity, root flare condition, structural defects invisible from above. Quadcopter drones deploy to orbit individual crowns — canopy volume, crown health, deadwood, upper crown dieback. The data feeds a persistent spatial model that tracks every tree through its lifecycle.

The arborist doesn't walk the district anymore. They review the dashboard. Hazard trees are flagged with point clouds. Growth tracking shows which newly planted trees are thriving and which need intervention. The canopy model quantifies progress toward the city's equity goals by census tract.

Ground + Air Architecture

Ground Platform Quadruped + terrestrial LiDAR. Trunk, lower stem, root flare, structural base.
Aerial Platform Quadcopter drones deploying from ground unit. Crown interior, upper canopy, overhead extent.
Fusion Layer Merges ground and aerial point clouds into complete tree model from root flare to crown tip.
Processing Pipeline DBH extraction, height, species, condition assessment, hazard scoring.
Monitoring Layer Longitudinal change detection, growth tracking, condition trending across every tree.

Development Roadmap

Phase 1 Year 1

iPad Urban MVP

Native iOS app using ARKit. Walk a street scanning individual trees. Validates the processing pipeline on urban specimens — isolated crowns, maintained sight lines, paved access. Test lab: Denver metro west suburbs.

>95%Detection
<2.5cmDBH RMSE
~$1,400Hardware
Phase 2 Years 2–3

Ground + Air Integration

Quadruped robot with mounted LiDAR + quadcopter for crown assessment. SLAM navigation on sidewalks — flat, paved, predictable. Merged ground+air point cloud produces complete tree digital twin. Denver metro municipal partners.

<$10KHardware
Ground+AirFusion
Phase 3 Years 3–5

Autonomous Urban Fleet

Fleet deployment by district, operating overnight. Passive sensor packages on existing city vehicles — garbage trucks, utility crews, street sweepers — collect baseline canopy data on every route, every week. Dedicated robots handle detailed structural assessment. Cities buy the platform and manage it, or run the cloud-hosted version. Expand to utility corridor monitoring.

$50B+TAM
19,000Municipalities
$28BUtility Veg Mgmt
Phase 4 Extension

Forest Extension

Same pipeline, harder terrain. Processing algorithms proven on 100,000 urban trees transfer directly to operational forestry. Quadruped navigates forest floor instead of sidewalks. Urban revenue funds the R&D that makes the forest product possible.

$5.8BForestry TAM
Same IPPipeline
CensusNot Sampling
03 Papers

Working Papers & Product Concepts

Working papers, prototypes, and product concepts advancing NRMTech — from defining the category and building autonomous data acquisition systems to designing AI-augmented tools for natural resource management.

The Gun-Deck Problem: Data Provenance Failures in Timber Inventory

A labor economics and institutional design analysis. Applies the Shapiro–Stiglitz shirking model and Holmström's moral hazard framework to forest inventory, demonstrating that the wage structure, monitoring capacity, and employment terms of federal and state timber cruising programs fall below the no-shirking condition — producing systematic data quality degradation as an equilibrium outcome, not a workforce discipline failure. Traces the consequences through commercial timber markets, carbon accounting, and institutional timberland portfolios exceeding $90 billion.

Read the Working Paper

NRMTech: The Case for Cognitive Infrastructure in Natural Resource Management

Introduces NRMTech as a sector category. Makes the case that natural resource management needs its own technology identity — the way AgTech and FinTech gave agriculture and finance theirs. Argues the core problem in natural resource management is a systemic failure of cognitive infrastructure — broken measurement systems, unverifiable field data, and misaligned incentive architectures that systematically degrade data quality. Maps the emerging technology landscape and makes the case that autonomous sensing doesn't just improve efficiency — it restructures the institutional conditions that make human-collected data unreliable.

Read the White Paper

Silvibot: Autonomous Ground-Based LiDAR Platform for Complete Forest Census

Product introduction and competitive analysis. Makes the case that 80-year-old sampling methods covering 2–5% of stems should be replaced by complete forest census — every tree measured, georeferenced, and backed by verifiable point cloud data. Describes a hardware-agnostic processing pipeline as the core IP, positions SilviBot against the full competitive landscape from traditional cruising to drone LiDAR, and presents the business case across a $2.2B addressable market.

Read the White Paper
Concept

Urban Trees as Infrastructure: The Case for Continuous Autonomous Monitoring in Municipal Forestry

Frames the urban tree data gap — 19,000+ municipalities managing canopy with windshield surveys and 5–10 year inspection cycles. Presents SilviBot-Urban as the data collection layer that feeds ArboStar, TreeKeeper, i-Tree, and every municipal GIS. Maps the $50B+ market across utility vegetation management, municipal forestry, and ecosystem services.

Concept

SilviBot as Urban Forestry Infrastructure: Continuous Monitoring for the Questions Cities Can't Currently Answer

Need a disease risk study on emerald ash borer spread across your city? Ask SilviBot to examine affected areas and model a mitigation strategy. Need a canopy coverage assessment by census tract for an equity grant? Ask SilviBot. Need to know when a large cohort of street trees planted in the 1970s will reach structural mortality? Ask SilviBot. The platform turns tree inventory from a periodic project into a persistent, queryable dataset.

Concept

The NRMTech Labor Thesis: How Automation Creates Better Roles, Not Fewer Jobs

The municipal arborist reviewing SilviBot data instead of driving 200 miles of street frontage per week. The ISA Certified Arborist validating sensor outputs instead of climbing every tree. Addresses the workforce displacement criticism head-on.

04 The Intersection

About

I've cruised timber on national forests, led enterprise technology modernization across federal agencies, and now manage an AI & Data Platform for the Department of the Interior. My background is the intersection of trees and systems — forestry fieldwork, economics, and large-scale data infrastructure.

The problem I keep finding is the same everywhere: natural resource decisions depend on data that is expensive to collect, difficult to verify, and out of date by the time it's used. Municipal arborists, utility vegetation managers, and federal foresters all face the same gap — the tools they have can't keep up with the trees they're responsible for.

I'm building SilviBot to close that gap. Autonomous tree inventory starting on city streets and extending into national forests. The first test lab is my neighborhood in the Denver metro, where I've planted 70 trees including 50 apple varieties that serve as a longitudinal monitoring dataset. The first customers are municipalities I can drive to in 15 minutes. The processing pipeline that inventories a city's street trees is the same pipeline that will eventually census a timber sale on a municipal, state, or national forest. NRMTech is the category that connects urban canopy management, utility vegetation intelligence, carbon verification, and operational forestry into a single technology ecosystem. I'm looking for municipal partners, research collaborators, and investors who see the same convergence.

Get in touchcontact@martinhensley.com

EducationM.S. Forestry, 2013Michigan Technological UniversityB.A. Economics, 2008Colorado State University
CurrentData Platform ManagerU.S. Department of the Interior
PreviousEconomistUSDA National Organic ProgramU.S. Department of the InteriorUSCG Headquarters, Washington D.C.IT Project ManagerUSDA Agricultural Research ServiceU.S. Department of the Interior
OperationsForesterU.S. Forest ServiceStudent Researcher, HPC & Computational PhysicsDOE National LaboratoryMarine Science TechnicianUSCG Sector San FranciscoDeck ForceUSCG Cutter Reliance (WMEC-615)Wilderness RangerU.S. Forest Service