Precise knowledge of fuel conditions is important for predicting fire hazards and simulating fire growth and intensity across the landscape. We present a methodology to retrieve and map forest canopy fuel and other forest structural parameters using small-footprint full-waveform airborne light detection and ranging (LiDAR) data. Full-waveform LiDAR sensors register the complete returned backscattered signal through time and can describe physical properties of the intercepted objects. This study was undertaken in a mixed forest dominated by Douglas-fir, occasionally mixed with other conifers, in north-west Oregon (United States). We extracted two sets of LiDAR metrics using pulse detection and waveform modelling and then constructed several predictive models using forward stepwise multiple linear regression. The resulting models explained ~80% of the variability for many of the canopy fuel and forest structure parameters: aboveground biomass (R2=0.84), quadratic mean diameter (R2=0.82), canopy height (R2=0.79), canopy base height (R2=0.78) and canopy fuel load (R2=0.79). The lowest performing models included basal area (R2=0.76), stand volume (R2=0.73), canopy bulk density (R2=0.67) and stand density index (R2=0.66). Our results indicate that full-waveform LiDAR systems show promise in systematically characterising the structure and canopy fuel loads of forests, which may enable accurate fire behaviour forecasting that in turn supports the development of prevention and planning policies.

Abstract:In precision forestry, tree species identification is key to evaluating the role of forest ecosystems in the provision of ecosystem services, such as carbon sequestration and assessing their effects on climate regulation and climate change. In this study, we investigated the effectiveness of tree species classification of urban forests using aerial-based HyMap hyperspectral imagery and light detection and ranging (LiDAR) data. First, we conducted an object-based image analysis (OBIA) to segment individual tree crowns present in LiDAR-derived Canopy Height Models (CHMs). Then, hyperspectral values for individual trees were extracted from HyMap data for band reduction through Minimum Noise Fraction (MNF) transformation which allowed us to reduce the data to 20 significant bands out of 118 bands acquired. Finally, we compared several different classifications using Random Forest (RF) and Multi Class Classifier (MCC) methods. Seven tree species were classified using all 118 bands which resulted in 46.3% overall classification accuracy for RF versus 79.6% for MCC. Using only the 20 optimal bands extracted through MNF, both RF and MCC achieved an increase in overall accuracy to 87.0% and 88.9%, respectively. Thus, the MNF band selection process is a preferable approach for tree species classification when using hyperspectral data. Further, our work also suggests that RF is heavily disadvantaged by the high-dimensionality and noise present in hyperspectral data, while MCC is more robust when handling high-dimensional datasets with small sample sizes. Our overall results indicated that individual tree species identification in urban forests can be accomplished with the fusion of object-based LiDAR segmentation of crowns and hyperspectral characterization.

While aboveground biomass and forest productivity can vary over abiotic gradients (e.g., temperature and moisture gradients), biotic factors such as biodiversity and tree species stand dominance can also strongly influence biomass accumulation. In this study we use a permanent plot network to assess variability in aboveground carbon (C) flux in forest tree annual aboveground biomass increment (ABI), tree aboveground net primary productivity (ANPPtree), and net soil CO2 efflux in relation to diversity of coniferous, deciduous, and a nitrogen (N)-fixing tree species (Alnus rubra). Four major findings arose: (1) overstory species richness and indices of diversity explained between one third and half of all variation in measured aboveground C flux, and diversity indices were the most robust models predicting measured aboveground C flux; (2) trends suggested decreases in annual tree biomass increment C with increasing stand dominance for four of the five most abundant tree species; (3) the presence of an N-fixing tree species (A. rubra) was not related to changes in aboveground C flux, was negatively related to soil CO2 efflux, and showed only a weak negative relationship with aboveground C pools; and (4) stands with higher overstory richness and diversity typically had higher soil CO2 efflux. Interestingly, presence of the N-fixing species was not correlated with soil inorganic N pools, and inorganic N pools were not correlated with any C flux or pool measure. We also did not detect any strong patterns between forest tree diversity and C pools, suggesting potential balancing of increased C flux both into and out of diverse forest stands. These data highlight variability in second-growth forests that may have implications for overstory community drivers of C dynamics