PyroCarbon3D

Wildland fires burn every year 3-5 million km2 around the world, releasing 2.2 Pg of carbon (C) into the atmosphere (equivalent to 25-30% of the global emissions from fossil fuel combustion). Accurate assessments of carbon emissions from wildfires are therefore essential to fully represent this ecosystem perturbation in models. This will allow to improve our climate forecasting abilities and to develop mitigation strategies for climate change. One of the key parameters to estimate carbon emissions is the amount of biomass (i.e. fuel) that is consumed and the C within it. Traditional field measurements of fuel and carbon consumption require long time and are not spatially explicit. 

To advance the quantification of how much carbon is in the vegetation fuels and how much is consumed during fires using point clouds and artificial intelligence.

In this project we are developing new methodologies that will advance carbon emission estimations from wildfires by providing more accurate and spatially explicit quantification of fuel and carbon consumption. We use a combination of state-of-the-art technology for 3D terrestrial point clouds and traditional field-based measurements and acquire field data in prescribed and experimental fires from low to high intensity in shrubland and forests in Spain, UK, US and Canada. With this information, fuel biomass and carbon, fuel consumption completeness and carbon emissions 3D maps are being developed. 

PyroCarbon3D – Advancing carbon emission estimations from wildfires applying artificial intelligence to 3D terrestrial point clouds (PID2021-126790NB-I00). Call for Knowledge Generation Projects 2021. Funding institution: Ministerio de Ciencia e Innovación (Spanish Government). Duration: 36 months.

Laíño, D.; Cabo, C.; Prendes, C.; Janvier, R.; Ordóñez, C.; Nikonovas, T.; Doerr, S.; Santín, C. 2024. 3DFin: a software for automated 3D forest inventories from terrestrial point clouds. Forestry 1-18 DOI: 10.1093/forestry/cpae020

3DFin: open source software for automated 3D forest inventories from terrestrial point clouds. Available as standalone software, Python package, and plug-in in CloudCompare and QGis: https://github.com/3DFin