ForestPlots.net supports researchers worldwide in managing long-term forest inventory data with a focus on tropical forests. Hosting one of the longest and most comprehensive records of forest change from 1939–2026, the platform tracks 4 million trees across 9,500 forest plots and 20,000 species, many in remote regions of the tropics. These long-term censuses are essential for monitoring biodiversity change, estimating carbon stocks, and understanding forest dynamics. By combining quality-assured long-term records and a distributed ownership model, ForestPlots.net facilitates rigorous, equitable and impactful worldwide research (ForestPlots.net et al. 2021).

ForestPlots.net’s success and wide acceptance stem from using highly standardised formats to allow common workflows and analyses from data collected by thousands of different researchers conducting on-the-ground measurements on individual trees in over 20 tropical countries. This data has been used to reveal, for example, the role of remaining intact tropical forests in absorbing globally significant quantities of carbon from the atmosphere and slowing climate change (Hubau et al. 2020, Nature). Current work is assessing the vulnerability of this global carbon sink to ongoing climate change and whether we are approaching a tipping point where remaining forests could become a carbon source.

However, collecting data in a fully compliant structure is difficult when relying on paper forms. Paper workflows introduce transcription errors, require extensive post-processing, and make it challenging to precisely locate trees in the field—issues intensified by remote working conditions with limited internet and electricity and frequent wet conditions. Given the global scale and diversity of researchers collaborating with ForestPlots.net, improving data capture at the source has substantial potential impact.

To address these challenges, we are developing a digital data collection workflow using R and QGIS for structured data preparation and QField for field-ready mobile surveys. This system allows teams to record standardised ForestPlots.net variables directly on Android tablets, capture required and optional attributes and accurately identify tree locations in the forest. Early testing indicates that the QGIS/QField workflow significantly reduces collection-to-upload time and improves data quality by eliminating manual digitisation, while complementary herbarium specimen collection provides an independent check on species identification.

This presentation demonstrates the workflow: preparing project templates in QGIS, structuring attribute forms according to ForestPlots.net standards, deploying projects via QField, and synchronising outputs back into the ForestPlots.net database. We will also share lessons learned from on-the-ground training activities and data collection, including device recommendations, offline operation strategies, common user pitfalls, and practical tips for challenging field environments.

Looking ahead, we are developing an RShiny tool to allow users to upload ForestPlots.net outputs and automatically generate QField-ready projects without needing R experience. By transitioning from paper to a spatially aware digital workflow, ForestPlots.net aims to improve efficiency, consistency, and data quality in long-term tropical forest monitoring and climate and biodiversity research.