Modelling the vertical distribution of canopy fuel load using national forest inventory and low-density airbone laser scanning data

Abstract

The fuel complex variables canopy bulk density and canopy base height are often used to predict crown fire initiation and spread. Direct measurement of these variables is impractical, and they are usually estimated indirectly by modelling. Recent advances in predicting crown fire behaviour require accurate estimates of the complete vertical distribution of canopy fuels. The objectives of the present study were to model the vertical profile of available canopy fuel in pine stands by using data from the Spanish national forest inventory plus low-density airborne laser scanning (ALS) metrics. In a first step, the vertical distribution of the canopy fuel load was modelled using the Weibull probability density function. In a second step, two different systems of models were fitted to estimate the canopy variables defining the vertical distributions; the first system related these variables to stand variables obtained in a field inventory, and the second system related the canopy variables to airborne laser scanning metrics. The models of each system were fitted simultaneously to compensate the effects of the inherent cross-model correlation between the canopy variables. Heteroscedasticity was also analyzed, but no correction in the fitting process was necessary. The estimated canopy fuel load profiles from field variables explained 84% and 86% of the variation in canopy fuel load for maritime pine and radiata pine respectively; whereas the estimated canopy fuel load profiles from ALS metrics explained 52% and 49% of the variation for the same species. The proposed models can be used to assess the effectiveness of different forest management alternatives for reducing crown fire hazard.

Integration of National Forest Inventory and Nationwide Airborne Laser Scanning Data to Improve Forest Yield Predictions in North-Western Spain

“…In this respect, Gobakken and Naesset [10] observed that larger plot sizes (300-400 m 2 ) compensate for errors in locating plots sampled for estimation of biophysical properties from ALS data. Thus, as the plot size used in the SNFI is around 1964 m 2 , for positioning errors of 5 and 10 m (much larger than the theoretical error), the areas overlapping a plot in a correct position and a plot located in an altered position are 84.3% and 74.7% respectively [25]. Although this may suggest that SNFI plot positioning is not difficult, practical errors will actually be much higher than theoretical errors.…”

“…The plot positioning procedures used in the SNFI-4 were applied by using handheld GPS equipment, which has an expected average accuracy of approximately 3-5 m [25]. In this respect, Gobakken and Naesset [10] observed that larger plot sizes (300-400 m 2 ) compensate for errors in locating plots sampled for estimation of biophysical properties from ALS data.…”

“…Moreover, ALS data, which cover the whole country, have been compiled by the PNOA-LiDAR project of the Instituto Geográfico Nacional (IGN) since 2008 with multipurpose objectives, e.g., obtaining digital elevation, surface or hydrographic models, and automatic detection of terrain modifications. Despite the availability of this very valuable information, very few studies have used both types of information together (e.g., [4,25,26]), mainly because of the following important challenges: (i) differences in time of acquisition of both types of data in most regions, and (ii) problems associated with the precise geolocation of SNFI plots.…”

Analysis of the occurrence of wildfires in the Iberian Peninsula based on harmonised data from national forest inventories

“…In a later study and using the same data, Fernandes (2009) examined the hypothesis of whether standard forest inventory data could be used to assess fire hazard. González-Ferreiro et al (2017) used data from the Fourth Spanish National Forest Inventory (SNFI4) to model the vertical profile of canopy fuels loads, with stand variables as predictors, in pure and even-aged P. pinaster and Pinus radiata D. Don stands in Galicia (north-western Spain). In this study, dominant height was the main predictor for both species and when other predictor variables were also included in models, only quadratic mean diameter for P. pinaster and stand basal area for P. radiata significantly improved the results obtained with dominant height.…”

“…Some studies in Europe have related the stand characteristics with the crown fire potential (Fernandes 2009;Ruiz-González and Álvarez-González 2011; Fernández-Alonso González-Ferreiro et al 2017), but only a few with fire probability (González et al 2006;Silva et al 2009).…”