DigiForest: Digital Analytics and Robotics for Sustainable Forestry

arXiv cs.RO / 4/17/2026

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Key Points

DigiForest proposes a large-scale precision forestry approach that uses digital technologies and autonomous robotics to support sustainable forest management goals in Europe and beyond.
The system combines heterogeneous autonomous robots (aerial, legged, and marsupial) to collect tree-level data and automate the building of forest inventories.
It includes automated extraction of tree traits plus a Decision Support System (DSS) to forecast forest growth and improve decision-making.
DigiForest also targets low-impact selective logging through purpose-built autonomous harvesters, and reports extensive real-world validation in Finland, the UK, and Switzerland.
By integrating data collection, inventorying, forecasting, and harvesting, DigiForest aims to address key challenges in forestry while promoting climate resilience and biodiversity benefits.

Abstract

Covering one third of Earth's land surface, forests are vital to global biodiversity, climate regulation, and human well-being. In Europe, forests and woodlands reach approximately 40% of land area, and the forestry sector is central to achieving the EU's climate neutrality and biodiversity goals; these emphasize sustainable forest management, increased use of long-lived wood products, and resilient forest ecosystems. To meet these goals and properly address their inherent challenges, current practices require further innovation. This chapter introduces DigiForest, a novel, large-scale precision forestry approach leveraging digital technologies and autonomous robotics. DigiForest is structured around four main components: (1) autonomous, heterogeneous mobile robots (aerial, legged, and marsupial) for tree-level data collection; (2) automated extraction of tree traits to build forest inventories; (3) a Decision Support System (DSS) for forecasting forest growth and supporting decision-making; and (4) low-impact selective logging using purpose-built autonomous harvesters. These technologies have been extensively validated in real-world conditions in several locations, including forests in Finland, the UK, and Switzerland.