José A. Vega;Stéfano Arellano-Pérez;Juan Gabriel Álvarez González;Cristina Fernández;Enrique Jiménez;P. Cuíñas;José María Fernández-Alonso;Daniel José Vega-Nieva;Fernando Castedo‐Dorado;Cecilia Alonso-Rego;Teresa Fontúrbel;Ana Daría Ruiz-Gonzále

Centro de Investigación Forestal de Lourizán, PO Box 127, 36080, Pontevedra, Spain;Unidad de Gestión Ambiental y Forestal Sostenible (UXAFORES), Departamento de Ingeniería Agroforestal, Escuela Politécnica Superior de Ingeniería, Universidad de Santiago de Compostela, Campus Universitario s/n, 27002, Lugo, Spai;Unidad de Gestión Ambiental y Forestal Sostenible (UXAFORES), Departamento de Ingeniería Agroforestal, Escuela Politécnica Superior de Ingeniería, Universidad de Santiago de Compostela, Campus Universitario s/n, 27002, Lugo, Spain;Facultad de Ciencias Forestales, Universidad Juárez del Estado de Durango, Río Papaloapan y Blvd. Durango s/n, Col. Valle del Sur, 34120, Durango, Mexico;Departamento de Ingeniería y Ciencias Agrarias, Universidad de León, Campus de Ponferrada, 24401, Ponferrada, Spain

Area-based approach;Additive equation systems;Live and dead fine fuel;Pinus pinaster;Pinus radiata;Pinus sylvestris;LITTER DECOMPOSITION;NORTHERN ARIZONA;ULEX-EUROPAEUS;CANOPY CLOSURE;PONDEROSA PINE;UNITED-STATES;FIRE BEHAVIOR;OVERSTORY;BIOMASS;TEMPERAT

In this study, 310 destructively sampled plots were used to develop two equation systems for the three main pine species in NW Spain (P. pinaster; P. radiata and P. sylvestris): one for estimating loads of understorey fuel components by size and condition (live and dead) and another one for forest floor fuels. Additive systems of equations were simultaneously fitted for estimating area-based fuel loads using overstorey, understorey and forest floor variables as regressors. The systems of equations included both the effect of pine species and the effect of understorey compositions dominated by ferns-brambles or by woody species, due to their obvious structural and physiological differences. In general, the goodness-of-fit statistics indicated that the estimates were reasonably robust and accurate for all of the fuel fractions. The best results were obtained for total understorey vegetation, total forest floor and raw humus fuel loads, with more than 76% of the observed variability explained, whereas the poorest results were obtained for coarse fuel loads of understory vegetation with a 53% of observed variability explained. To reduce the overall costs associated with the field inventories necessary for operational use of the models, the additive systems were fitted again using only overstorey variables as potential regressors. Only relationships for fine (<6 mm) and total understorey vegetation and total forest floor fuel loads were obtained, indicating the complexity of the forest overstorey-understorey and overstorey-forest floor relationships. Nevertheless, these models explained around 52% of the observed variability. Finally, equations estimating the total understorey vegetation and the total forest floor fuel loads based only on canopy cover were fitted. These models explained only 26%–32% of the observed variability; however, their main advantage is that although understorey vegetation in forested landscapes is largely invisible to remote sensing, canopy cover can be estimated with moderate accuracy, allowing for landscape-scale estimates of total fuel loads. The equations represent an appreciable advance in understorey and forest floor fuel load assessment in the region and areas with similar characteristics and may be instrumental in generating fuel maps, fire management improvement and better C storage assessment by vegetation, among many other uses.