Modelling canopy fuel variables for Pinus radiata D. Don in NW Spain with low-density LiDAR data

Abstract

Crown fire initiation and spread are key elements in gauging fire behaviour potential in conifer forests. Crown fire initiation and spread models implemented in widely used fire behaviour simulation systems such as FARSITE and FlamMap require accurate spatially explicit estimation of canopy fuel complex characteristics. In the present study, we evaluated the potential use of very low-density airborne LiDAR (light detection and ranging) data (0.5 first returns m–2) – which is freely available for most of the Spanish territory – to estimate canopy fuel characteristics in Pinus radiata D. Don stands in north-western Spain. Regression analysis indicated strong relationships (R2=0.82–0.98) between LiDAR-derived metrics and field-based fuel estimates for stand height, canopy fuel load, and average and effective canopy base height Average and effective canopy bulk density (R2=0.59–0.70) were estimated indirectly from a set of previously modelled forest variables. The LiDAR-based models developed can be used to elaborate geo-referenced raster files to describe fuel characteristics. These files can be generated periodically, whenever new freely available airborne LiDAR data are released by the Spanish National Plan of Aerial Orthophotography, and can be used as inputs in fire behaviour simulation systems.

Estimación de variables de combustible de copa y de masa, caracterizando el efecto de las claras en su estructura usando LiDAR aerotransportado

“…The goodness-of-fit statistics obtained for the models of canopy fuel load (R 2 adj = 0.97, RMSE = 0.0363), "effective" canopy base height and canopy base height (R 2 adj > 0.95, RMSE ranged from 0.178 to 0.329 m, respectively), and canopy bulk density and "effective" canopy bulk density (R 2 adj > 0.94, RMSE 0.00892 and 0.00832, respectively), were slightly better than those reported by Naesset and Økland (2002) in spruce Norway boreal forests, Andersen et al (2005) in Douglas-fir Pacific Northwest forests, Hall et al (2005) in ponderosa pine forests of Colorado, Peterson et al (2005) in mixed coniferous forests of California, Zhao et al (2011) in Eastern Texas forest of loblolly pine, González-Olabarría et al (2012) in central Spain forests of European black pine and maritime pine, González-Ferreiro et al (2014) in northwest Spain forests of radiata pine, or Ruiz et al (2014b) in Douglas-fir and mixed forest in Northern Oregon. Nevertheless, our results should be treated with caution because of the scarce number of plots analysed.…”

“…In this sense, LiDAR systems have been demonstrated to be capable of accurate and efficient estimation of canopy fuel variables over large areas (e.g. Andersen et al, 2005;González-Ferreiro et al, 2014), since they can provide spatially-explicit detailed three-dimensional information about the size and structure of the forest canopy (Reitberger et al, 2008;Wagner et al, 2008). In fact, they offer an alternative to traditional fieldwork for estimating canopy fuel characteristics, because they can provide comprehensive spatial coverage which is very useful in spatially-explicit fire behaviour simulator systems such as FARSITE (Finney, 2004) and FlamMap (Finney, 2006).…”

“…Naesset and Økland, 2002;Andersen et al, 2005;Hall et al, 2005;González-Ferreiro et al, 2014) and even to classify forest species (Holmgren and Persson, 2004). To the best of our knowledge, there are not studies that use height distribution to identify managed and unmanaged forest stands neither to estimate stand and canopy fuel variables in Pinus pinaster Ait.…”

Estimación de las existencias maderables de Pinus radiata a escala provincial utilizando datos LiDAR de baja resolución

“…Aun así, Fabra (2012) afirma que, en España, el inventario forestal con datos LiDAR puede abaratar los costes hasta aproximadamente los 5 € ha -1 . Además, la posibilidad de contar con datos LiDAR a nivel nacional puede reducir más aún los costes y fomentar el uso múltiple de los datos LiDAR (los costes unitarios disminuyen a medida que aumenta la superficie escaneada y el número de objetivos propuestos para cada vuelo) (González-Ferreiro et al 2014). En el caso de los datos a nivel nacional del PNOA, las consultas realizadas al Instituto Geográfico Nacional Español (IGN) revelaron que, para grandes adquisiciones (900.000 ha), el coste de los datos LiDAR es de aproximadamente 0,14 € ha -1 en la mayor parte de los casos (duplicándose en el caso de adquisiciones centradas en áreas muy perfiladas e irregulares o pequeñas islas del territorio).…”

“…Los datos LiDAR del PNOA han demostrado su utilidad para la estimación de un gran conjunto de variables de rodal como el volumen (Guerra-Hernández et al 2016a), el área basimétrica (Guerra-Hernández et al 2016a), la altura media de Lorey (González-Ferreiro et al 2014, Guerra-Hernández et al 2016a, variables del complejo de combustibles de copa (González-Ferreiro et al 2014), la severidad del fuego (Montealegre et al 2014), la biomasa o el carbono (Montealegre et al 2015, pero no han sido usados aún en España para el inventario forestal de grandes superficies, como la provincia de Lugo.…”