Editor's note
This week feels operational.
Several stories show digital forestry moving from pilots and datasets into systems that actively change how forests are managed. Autonomous harvesting cycles, commercial Forest 4.0 showcases, ultra-detailed structural datasets, and global disturbance monitoring all point in the same direction: tools are no longer experimental—they are executable.
What stands out is spatial and temporal granularity. Whether in thinning, urban tree mapping, or global disturbance tracking, decisions are becoming more precise in both where and when they are applied.
Axel
WHAT GOT ME THINKING
AirForestry Hits Full Harvest Milestone and Ramps Up Engineering Team
Published February 26–28, AirForestry shared two major updates in quick succession. First, footage of their electric drone completing a full autonomous harvest cycle—flying to a tree, sawing it, lifting the trunk, and transporting it to the roadside without human input. CEO Olle Gelin called it something "that can't be described in words." Days later, the company announced open positions for drone technicians, hardware developers, and software engineers—a deliberate scale-up following the SEK 28M Series A pre-commitment announced February 20.
Axel’s notes: What makes this particularly interesting from a forest management perspective is what it could enable.
If aerial harvesting becomes reliable and economically viable, thinning no longer needs to be treated as a stand-level operation executed two or three times per rotation. It could evolve into a more continuous intervention model. removing trees only where and when density, competition, or risk indicates it is optimal.
Higher spatial granularity and better timing could translate into improved overall production and stand stability. The real shift is not just automation—it is optionality in silvicultural design.
UCL Releases World's Most Detailed 3D Tropical Forest Dataset
UCL released ForestScan—the most detailed 3D tree census ever collected, combining terrestrial laser scanning, UAV-LiDAR, and airborne LiDAR across tropical, temperate, and boreal sites. The open dataset delivers millimetre-level structural models of individual trees and will directly calibrate ESA's Biomass satellite to improve global carbon stock estimates derived from orbit.
terraPulse's Joe Sexton Lifts the Lid on Global Forest Disturbance Mapping
Joe Sexton of terraPulse shared new figures from Feng et al. (2026) on March 1, giving rare behind-the-scenes insight into the AI and Landsat time-series methodology that powers the study's global forest disturbance and recovery maps. Sexton walks through how the model detects annual changes in forest cover and biomass at 30-metre resolution worldwide—explaining the technical choices, data challenges, and validation steps that make near-real-time deforestation alerts and EUDR supply chain compliance tools possible at global scale.
Arbomapper Spun Out of Purdue's 280-Million-Tree AI City Map
Published February 17, Purdue's digital forestry team mapped 280 million urban trees across 330+ U.S. cities using PlanetScope satellite data and AI, achieving 92.5% count accuracy and locating trees to within 1.5 metres—processing all 330 cities in under a day. NYC's Million Trees Initiative took 2,000 volunteers 30,000 hours to count street trees in one city alone. The work has been commercialised as Arbomapper, co-founded by professors Songlin Fei and Ayman Habib.
INTERFORST 2026: Forest 4.0 Moves from Concept to Commercial Reality
Munich's INTERFORST (October 2026) has published its Forest 4.0 thematic programme, showcasing how digital forestry has crossed from R&D into operational tools. The programme covers GIS platforms, drone-based LiDAR inventory, AI bark beetle detection, digital twin vitality monitoring, and full timber supply chain integration. A dedicated startup area will feature emerging companies developing practical solutions for the industry.