Tree-Quest: A Citizen Science App for Collecting Single-Tree Attributes

Accurate quantification of single-tree structural attributes is essential for improving estimates of terrestrial carbon stocks and for supporting sustainable forest and urban tree management. While traditional forest inventory methods and advanced technologies, such as terrestrial laser scanning (TLS) provide high-quality measurements, their spatial and temporal coverage remains limited due to cost and logistical constraints. Citizen science offers an underexploited opportunity to complement expert-based data collection and enhance data availability at large scales.

We present an overview of recent advances in integrating citizen science with digital tools and remote sensing for single-tree assessment, with a particular focus on urban environments. Our contribution specifically explores the use of mobile applications, low-cost sensors, and participatory approaches to support crowdsourced identification of tree species diversity and mapping of vegetation carbon stocks in urban environments.

To this end, we developed Tree-Quest (TQ), a free citizen-science mobile application, designed to measure single-tree attributes, such as tree species (ID), tree height (TH) and stem diameter at breast height (DBH). We compiled a dataset comprising 700 measurements acquired from 30 volunteers across peri-urban landscapes located in the vicinity of Vienna. Volunteers achieved a mean absolute error (MAE) of 3 cm for DBH (R² = 0.97; rMAE = 6%) and 1.5 m for TH (R² = 0.91; rMAE = 11%), thus demonstrating comparable measurement accuracy with traditional forest inventory.

Our findings indicate the potential of citizen science to complement remote sensing estimates and forest inventory measurements, thus supporting climate adaptation strategies, and improving our understanding of tree-level carbon dynamics in urban environments, beyond traditional estimates derived from natural forest ecosystems.