2026-04-17T07:44:51Zhttps://recercat.cat/oai/request

oai:recercat.cat:10459.1/4672192025-09-15T18:25:02Zcom_2072_3622col_2072_479130

00925njm 22002777a 4500 dc Kutchartt, Erico author González-Olabarria, José Ramón author Trasobares, Antoni author Aquilué, Núria author Guerra-Hernández, Juan author Nunes, Leónia author Sequeira, Ana Catarina author Botequim, Brigite author Hauglin, Marius author Palaiologou, Palaiologos author Cardil Forradellas, Adrián author Rogai, Martino author Vassilev, Vassil author Pimont, Francois author Martin-Ducup, Olivier author Pirotti, Francesco author 2024 Canopy base height (CBH) and canopy bulk density (CBD) are forest canopy fuel parameters that are key for modeling the behavior of crown wildfires. In this work, we map them at a pan-European scale for the year 2020, producing a new dataset consisting of two raster layers containing both variables at an approximate resolution of 100 m. Spatial data from Earth observation missions and derived down-stream products were retrieved and processed using artificial intelligence to first estimate a map of aboveground biomass (AGB). Allometric models were then used to estimate the spatial distribution of CBH using the canopy height values as explanatory variables and CBD using AGB values. Ad-hoc allometric models were defined for this study. Data provided by FIRE-RES project partners and acquired through field inventories was used for validating the final products using an independent dataset of 804 ground-truth sample plots. The CBH and CBD raster maps have, respectively, the following accuracy regarding specific metrics reported from the modeling procedures: (i) coefficient of correlation (R) of 0.445 and 0.330 (p-value < 0.001); (ii) root mean square of error (RMSE) of 3.9 m and 0.099 kg m−3; and (iii) a mean absolute percentage error (MAPE) of 61% and 76%. Regarding CBD, the accuracy metrics improved in closed canopies (canopy cover > 80%) to R = 0.457, RMSE = 0.085, and MAPE = 59%. In short, we believe that the degree of accuracy is reasonable in the resulting maps, producing CBH and CBD datasets at the pan-European scale to support fire mitigation and crown fire simulations. This research was funded by the European Union’sHorizon 2020 Research and Innovation Programme through the project entitled “Innovative technologies & socio-ecological-economic solutions for fire resilient terri-tories in Europe – FIRE-RES” under grant agreement Nº101037419. The field data collected in Portugal was funded by the Foundation for Science and Technology(FCT), Portugal to Dr. Guerra-Hernández [#CEECIND/02576/2022]. Dra. Núria Aquilué was supported by a Juan de la Cierva fellowship of the Spanish Ministry of Science and Innovation [FCJ2020-046387-I]. Dr. Erico Kutchartt was supported by Fondazione Cassa di Risparmio di Padova e Rovigo (CARIPARO). Cloud cluster processing was supported by the Agritech National Research Center and received funding from the European Union Next-Generation EU [Piano Nazionale di Ripresae Resilienza (PNRR) – Missione 4 Componente 2, Investimento 1.4 – D.D. 1032 17/06/2022, CN00000022]. Satellite data Allometric equations Machine learning Aboveground biomass Forest fires Fire simulations Decision support systems Satellite-based mapping of canopy fuels at the pan-European scale