FORMEC 2026

Dynamic Single-Tree-Based Digital Twins: Integrating Harvester Production Data and Remote Sensing for Precision Forest Operations

17 Sept 2026, 09:00

18m

DP 106 - DP106 (CZU Prague, Czechia)

Oral presentation Paralel session 4

Speaker

Prof. Thomas Purfürst (Chair of Digitized Forestry Processes and Systems, Faculty of Environmental Sciences, Dresden University of Technology, Dresden, Germany)

Description

Precise forest management under the increasing pressures of climate change necessitates a transition from static inventory databases to dynamic, high-resolution digital twins. Current modelling approaches frequently rely on episodic remote sensing data, such as LiDAR or aerial photogrammetry, which suffer from temporal latency and fail to reflect the immediate structural changes following harvesting or stochastic disturbance events. This presentation introduces an operational framework for the management of dynamic, single-tree-based digital twins utilising the "DigiTreeS" data standard. This architecture incorporates a robust API designed to facilitate interoperability between various forestry management systems, including silvicultural decision-support tools, wood supply chain logistics, and predictive growth modelling.
The core innovation of the DigiTreeS system is an automated interface that leverages near real-time production data from harvesters during operational processing. By integrating geolocated harvester production data, the digital twin is updated instantaneously: harvested individuals are removed from the model, while the attributes of the residual stand, including diameter at breast height (DBH), stem quality, and curvature, are validated and refined through in-situ sensor measurements. Preliminary results indicate that this hybrid approach significantly enhances the accuracy of timber volume and assortment structure predictions compared to traditional models derived solely from remote sensing.
By shifting the digital twin from a static representation to a learning ecosystem, this research provides the foundational data infrastructure for adaptive forest management, transparent carbon certification, and precision logistics. Furthermore, the single-tree granularity of the model enables advanced visualization of thinning strategies and operational planning through virtual and augmented reality (VR/AR) interfaces. This presentation delineates the technical concept and evaluates the results achieved in experimental test areas, demonstrating the utility of dynamic single-tree digital twins as a transformative tool for modern forest operations.