Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data

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.This paper was developed as a result of two mobility grants funded by the Erasmus Mundus Programme of the European Commission under the Transatlantic Partnership for Excellence in Engineering (TEE Project) and the Generalitat Valenciana (BEST/2012/235). The authors appreciate the financial support provided by the Spanish Ministry of Science and Innovation in the framework of the project CGL2010-19591/BTE. In addition, the authors thank the Panther Creek Remote Sensing and Research cooperative program for the data provided for this research, Jim Flewelling (Seattle Biometrics) and George McFadden (Bureau of Land Management) for their help in data availability and preparation.Hermosilla Gómez, T.; Ruiz Fernández, LÁ.; Kazakova, AN.; Coops, N.; Moskal, LM. (2014). Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data. International Journal of Wildland Fire. 23(2):224-233. https://doi.org/10.1071/WF13086S224233232Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723. doi:10.1109/tac.1974.1100705Andersen, H.-E., McGaughey, R. J., & Reutebuch, S. E. (2005). Estimating forest canopy fuel parameters using LIDAR data. 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Quantifying vertical and horizontal stand structure using terrestrial LiDAR in Pacific Northwest forests

Thesis (Master's)--University of Washington, 2013Abstract Quantifying vertical and horizontal stand structure using terrestrial LiDAR in Pacific Northwest forests Alexandra Kazakova Chair of Supervisory Committee: Dr. L. Monika Moskal School of Environmental and Forest Science Stand level spatial distribution is a fundamental part of forest structure that influences many ecological processes and ecosystem functions. Vertical and horizontal spatial structure provides key information for forest management. Although horizontal stand complexity can be measured through stem mapping and spatial analysis, vertical complexity within the stand remains a mostly visual and highly subjective process. Tools and techniques in remote sensing, specifically LiDAR, provide three dimensional datasets that can help get at three dimensional forest stand structure. Although aerial LiDAR (ALS) is the most widespread form of remote sensing for measuring forest structure, it has a high omission rate in dense and structurally complex forests. In this study we used terrestrial LiDAR (TLS) to obtain high resolution three dimensional point clouds of plots from stands that vary by density and composition in the second-growth Pacific Northwest forest ecosystem. We used point cloud slicing techniques and object-based image analysis (OBIA) to produce canopy profiles at multiple points of vertical gradient. At each height point we produced segments that represented canopies or parts of canopies for each tree within the dataset. The resulting canopy segments were further analyzed using landscape metrics to quantify vertical canopy complexity within a single stand. Based on the developed method, we have successfully created a tool that utilizes three dimensional spatial information to accurately quantify the vertical structure of forest stands. Results show significant differences in the number and the total area of the canopy segments and gap fraction between each vertical slice within and between individual forest management plots. We found a significant relationship between the stand density and composition and the vertical canopy complexity. The methods described in this research make it possible to create horizontal stand profiles at any point along the vertical gradient of forest stands with high frequency, therefore providing ecologists with measures of horizontal and vertical stand structure. Key Words: Terrestrial laser scanning, canopy structure, landscape metrics, aerial laser scanning, lidar, calibration, Pacific Northwes

Diversity-Carbon Flux Relationships in a Northwest Forest

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