Prediction of Forest Canopy and Surface Fuels from Lidar and Satellite Time Series Data in a Bark Beetle-Affected Forest

Abstract

Wildfire behavior depends on the type, quantity, and condition of fuels, and the effect that bark beetle outbreaks have on fuels is a topic of current research and debate. Remote sensing can provide estimates of fuels across landscapes, although few studies have estimated surface fuels from remote sensing data. Here we predicted and mapped field-measured canopy and surface fuels from light detection and ranging (lidar) and Landsat time series explanatory variables via random forest (RF) modeling across a coniferous montane forest in Colorado, USA, which was affected by mountain pine beetles (Dendroctonus ponderosae Hopkins) approximately six years prior. We examined relationships between mapped fuels and the severity of tree mortality with correlation tests. RF models explained 59%, 48%, 35%, and 70% of the variation in available canopy fuel, canopy bulk density, canopy base height, and canopy height, respectively (percent root-mean-square error (%RMSE) = 12-54%). Surface fuels were predicted less accurately, with models explaining 24%, 28%, 32%, and 30% of the variation in litter and duff, 1 to 100-h, 1000-h, and total surface fuels, respectively (%RMSE = 37-98%). Fuel metrics were negatively correlated with the severity of tree mortality, except canopy base height, which increased with greater tree mortality. Our results showed how bark beetle-caused tree mortality significantly reduced canopy fuels in our study area. We demonstrated that lidar and Landsat time series data contain substantial information about canopy and surface fuels and can be used for large-scale efforts to monitor and map fuel loads for fire behavior modeling at a landscape scale.

Potential of Sentinel-2A Data to Model Surface and Canopy Fuel Characteristics in Relation to Crown Fire Hazard

“…This result was expected, due to the inability of optical sensors to penetrate the forest canopy [67], limiting their use for estimating surface fuel characteristics where tree canopies are present [12]. Similar goodness-of-fit statistics have been reported for other authors; for example, Jin and Chen [19] fitted linear models to estimate loads of different time-lag fuels, with percentages of variance explained ranging from 6% to 17% using Landsat TM data, and from 3% to 45% using high spatial resolution QuickBird data; Bright et al [77] reported percentages of variance explained ranging from 16% to 30% for different time-lag fuels in a coniferous montane forest in Colorado, US, using Random Forest and LiDAR-derived metrics. These percentages increased by 2-8% when Landsat time series variables were added to the model.…”

“…Pierce et al [14] reported values of 8% and 63% of observed CBH and CBD variance explained with RF models using Landsat 5 TM data and topographic features in conifer forests of north California, US. Bright et al [77], in a study of a coniferous montane forest in Colorado, US, obtained values of 28% and 46% of variance explained for CBH and CBD, respectively, with RF models and LiDAR-derived metrics. Falkowski et al [16] explained 47% of CBD-observed variance with linear models using ASTER images in a mixed temperate conifer forest in Idaho, USA.…”

Estimating Canopy Fuel Attributes from Low-Density LiDAR

“…Of the original 55 predictor variables, only one, describing the proportion of returns between 5 and 10 m (STRATUM5), was retained across all models. This finding is consistent with previous studies that have previously identified canopy strata return proportions as important across multiple models of forest attributes [79,80]. For the models of CFBH and LCBH, the most important variables are identical (LMOM1, STRATUM6).…”

Spatial distribution of surface forest fuel in the Slovak Republic

“…It is confirmed in the work of more experts, e.g. Bright et al (2017); Crespo-Peremarch, Ruiz, and Balaguer-Beser (2016), and Chen, Zhu, Yebra, Harris, and Tapper (2016).…”