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DigiForest is a precision forestry approach that integrates autonomous mobile robots (aerial, legged, and marsupial) for tree-level data collection with automated tree trait extraction to build forest inventories. This data feeds a Decision Support System (DSS) for forecasting forest growth and informing low-impact selective logging via autonomous harvesters. The system has been validated across diverse European forests, offering a pathway to more sustainable and resilient forest management.
Autonomous, multi-modal robots are poised to revolutionize forestry by providing unprecedented tree-level data for optimized and sustainable forest management.
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.
