Evaluation of alternative methods for using LiDAR to predict aboveground biomass in mixed species and structurally complex forests in northeastern North America

Rei Hayashi, John A Kershaw, Aaron Weiskittel


Light detection and ranging (LiDAR) has become a common means for predicting key forest structural attributes, but comparisons of alternative statistical methods and the spatial extent of LiDAR metrics extraction on independent datasets have been minimal. The primary objective of this study was to assess the performance of local and non-local LiDAR aboveground biomass (AGB) prediction models at two locations in the Acadian Forest. Two common statistical techniques, nonlinear mixed effects (NLME) and random forest (RF), were used to fit the prediction models and compared. Finally, this study evaluated the influence of alternative plot radii for LiDAR metrics extraction on model fit and prediction accuracy. AGB models were independently developed at each forest and tested both locally (model applied to same forest used for development) and non-locally (model applied to different forest) using an extensive network of ground-based plots. In general, RF was found to outperform NLME when applied locally, but the differences between the approaches were negligible when applied to the non-local dataset. NLME was found to perform equally well locally and non-locally. LiDAR extraction radius had very little influence on model performance as well. Minimal differences between models developed using fixed- and variable-radius methods were found, while the optimal LiDAR extraction radius was not consistent among forests, statistical technique, or local vs. non-local. Overall, the results highlight the importance of a robust calibration dataset that covers the full range of observed variation for developing accurate prediction models based on remote sensing data.


LiDAR; random forest; nonlinear mixed effects models; fixed-radius plots; variable-radius plots; Maine; New Brunswick;

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Asner, G.P., Clark, J.K., Mascaro, J., Galindo Garca, G.A., Chadwick, K.D., Navarrete Encinales, D.A., Paez-Acosta, et al. 2012. High-resolution mapping of forest carbon stocks in the Colombian Amazon. Biogeosciences 9, 2683–2696.

Asner, G.P., Hughes, R.F., Varga, T.A., Knapp, D.E., Kennedy-Bowdoin, T., 2009. Environmental and biotic controls over aboveground biomass throughout a tropical rain forest. Ecosystems 12, 261–278.

Asner, G.P., Mascaro, J., 2014. Mapping tropical forest carbon: Calibrating plot estimates to a simple LiDAR metric. Remote Sensing of Environment 140, 614–624.

Asner, G.P., Mascaro, J., Muller-Landau, H.C., Vieilledent, G., Vaudry, R., Rasamoelina, M., Hall, J.S., Breugel, M.v., 2012. A universal airborne LiDAR approach for tropical forest carbon mapping. Oecologia 168, 1147–1160.

Bollandsås, O.M., Maltamo, M., Gobakken, T., Næsset, E., 2013. Comparing parametric and non-parametric modelling of diameter distributions on independent data using airborne laser scanning in a boreal conifer forest. Forestry 86, 493–501.

Breiman, L., 2001. Random forest. Machine Learning 45, 5–32.

Brissette, J., Kenefic, L., Russell, M., Puhlick, J., 2012. Overstory tree and regeneration data from the “Silvicultural Effects on Composition, Structure, and Growth” study at Penobscot Experimental Forest. Newtown Square, PA: USDA Forest Service, Northern Research Station.

Chen, Q., Laurin, G. V, Battles, J.J., Saah, D., 2012. Integration of airborne lidar and vegetation types derived from aerial photography for mapping aboveground live biomass. Remote Sensing of Environment 121, 108–117.

Dalponte, M., Martinez, C., Rodeghiero, M., Gianelle, D., 2011. The role of ground reference data collection in the prediction of stem volume with LiDAR data in mountain areas. ISPRS Journal of Photogrammetry and Remote Sensing 66, 787–797.

De’ath, G., 2007. Boosted trees for ecological modeling and prediction. Ecology 88, 243–251.

Falkowski, M.J. 2015. Increasing the efficiency of LiDAR based forest inventories: A novel approach for integrating variable radius inventory plots with LiDAR Data. Abstract 70590. American Geophysical Union Fall Meeting. 14 – 18 December 2015. San Francisco, CA.

Falkowski, M.J., Hudak, A.T., Crookston, N.L., Gessler, P.E., Uebler, E.H., Smith, A.M.S., 2010. Landscape-scale parameterization of a tree-level forest growth model: a k-nearest neighbor imputation approach incorporating LiDAR data. Canadian Journal of Forest Research 40, 184–199.

Frazer, G.W., Magnussen, S., Wulder, M.A., Niemann, K.O., 2011. Simulated impact of sample plot size and co-registration error on the accuracy and uncertainty of LiDAR-derived estimates of forest stand biomass. Remote Sensing of Environment 115, 636–649.

Gobakken, T., Korhonen, L., Næsset, E., 2013. Laser-assisted selection of field plots for an area-based forest inventory. Silva Fennica 47, 1–20.

Gobakken, T., Næsset, E., 2008. Assessing effects of laser point density, ground sampling intensity, and field sample plot size on biophysical stand properties derived from airborne laser scanner data. Canadian Journal of Forest Research 38, 1095–1109.

Gobakken, T., Næsset, E., 2009. Assessing effects of positioning errors and sample plot size on biophysical stand properties derived from airborne laser scanner data. Canadian Journal of Forest Research 39, 1036–1052.

Hawbaker, T.J., Keuler, N.S., Lesak, A.A., Gobakken, T., Contrucci, K., Radeloff, V.C., 2009. Improved estimates of forest vegetation structure and biomass with a LiDAR-optimized sampling design. Journal of Geophysical Research-Biogeosciences 114, 1–11.

Hayashi, R., Weiskittel, A., Sader, S., 2014. Assessing the feasibility of low-density LiDAR for stand inventory attribute predictions in complex and managed forests of northern Maine, USA. Forests 5, 363–383.

Heidemann, H.K., 2012. Lidar base specification version 1.0: U.S. Geological survey techniques and methods, in: Book 11, Collection and Delineation of Spatial Data. p. 63.

Holmgren, J., Lindberg, E., 2013. Tree crown segmentation based on a geometric tree crown model for prediction of forest variables. Canadian Journal of Remote Sensing 39, S86–S98.

Holmgren, J., Nilsson, M., Olsson, H., 2003. Simulating the effects of lidar scanning angle for estimation of mean tree height and canopy closure. Canadian Journal of Remote Sensing 29, 623–632.

Hudak, A.T., Crookston, N.L., Evans, J.S., Hall, D.E., Falkowski, M.J., 2008. Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data. Remote Sensing of Environment 112, 2232–2245.

Hudak, A.T., Evans, J.S., Smith, A.M.S., 2009. LiDAR utility for natural resource managers. Remote Sensing 1, 934–951.

Jenkins, J.C., Chojnacky, D.C., Heath, L.S., Birdsey, R.A., 2003. National-scale biomass estimators for United States tree species. Forest Science 49, 12–35.

Kankare, V., Vastaranta, M., Holopainen, M., Räty, M., Yu, X., Hyyppä, J., Hyyppä, H., Alho, P., Viitala, R., 2013. Retrieval of forest aboveground biomass and stem volume with airborne scanning LiDAR. Remote Sensing 5, 2257–2274.

Kuhn, M., 2008. Building predictive models in R using the caret package. Journal of Statistical Software 28, 1–26.

Latifi, H., Nothdurft, A., Koch, B., 2010. Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: application of multiple optical/LiDAR-derived predictors. Forestry 83, 395–407.

Li, Y.Z., Andersen, H.E., McGaughey, R., 2008. A comparison of statistical methods for estimating forest biomass from light detection and ranging data. Western Journal of Applied Forestry 23, 223–231.

Liaw, A., Wiener, M., 2002. Classification and regression by randomForest. R News 2.

Loo, J., Ives, N., 2003. The Acadian forest: historical condition and human impacts. Forestry Chronicle 79, 462–474.

Magnussen, S., Boudewyn, P., 1998. Derivations of stand heights from airborne laser scanner data with canopy-based quantile estimators. Canadian Journal of Forest Research 28, 1016–1031.

Maltamo, M., Korhonen, K.T., Packalén, P., Mehtätalo, L., Suvanto, A., 2007. Testing the usability of truncated angle count sample plots as ground truth in airborne laser scanning-based forest inventories. Forestry 80, 73–81.

Maltamo, M., Packalén, P., Suvanto, A., Korhonen, K.T., Mehtätalo, L., Hyvönen, P., 2009. Combining ALS and NFI training data for forest management planning: a case study in Kuortane, Western Finland. European Journal of Forest Research 128, 305–317.

Means, J.E., Acker, S.A., Fitt, B.J., Renslow, M., Emerson, L., Hendrix, C.J., 2000. Predicting forest stand characteristics with airborne scanning lidar. Photogrammetric Engineering and Remote Sensing 66, 1367–1371.

Næsset, E., 2004a. Practical large-scale forest stand inventory using a small-footprint airborne scanning laser. Scandinavian Journal of Forest Research 19, 164–179. Næsset, E., 2004b. Accuracy of forest inventory using airborne laser scanning: evaluating the first Nordic full-scale operational project. Scandinavian Journal of Forest Research 19, 554–557.

Packalen, P., Mehtätalo, L., Maltamo, M., 2011. ALS-based estimation of plot volume and site index in a eucalyptus plantation with a nonlinear mixed-effect model that accounts for the clone effect. Annals of Forest Science 68, 1085–1092.

Penner, M., Pitt, D.G., Woods, M.E., 2013. Parametric vs. nonparametric LiDAR models for operational forest inventory in boreal Ontario. Canadian Journal of Remote Sensing 39, 426–443.

Peuhkurinen, J., Maltämo, M., Malinen, J., 2008. Estimating species-specific diameter distributions and saw log recoveries of boreal forests from airborne laser scanning data and aerial photographs: a distribution-based approach. Silva Fennica 42, 625–641.

Pinheiro, J., Bates, D., Debroy, S., Sarkar, D., 2014. nlme: Linear and nonlinear mixed effects models. R Package Version 3.1-117.

R Development Core Team, 2013. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria.

Rice, B., Weiskittel, A.R., Wagner, R.G., 2014. Efficiency of alternative forest inventory methods in partially harvested stands. European Journal of Forest Research 133, 261–272.

Robinson, A.P., Froese, R.E., 2004. Model validation using equivalence tests. Ecological Modelling 176, 349–358.

Ruiz, L.A., Hermosilla, T., Mauro, F., Godino, M., 2014. Analysis of the influence of plot size and LiDAR density on forest structure attribute estimates. Forests 5, 936–951.

Scrinzi, G., Clementel, F., Floris, A. 2015. Angle count sampling reliability as ground truth for area-based LiDAR applications in forest inventories. Canadian Journal of Forest Research 45: 506-514.

Sendak, P.E., Brissette, J.C., Frank, R.M., 2003. Silviculture affects composition, growth, and yield in mixed northern conifers: 40-year results from the Penobscot Experimental Forest. Canadian Journal of Forest Research 33, 2116–2128.

Stone, C., Penman, T., Turner, R., 2011. Determining an optimal model for processing lidar data at the plot level: results for a Pinus radiata plantation in New South Wales, Australia. New Zealand Journal of Forestry Science 41, 191–205.

Su, J.G., Bork, E.W., 2007. Characterization of diverse plant communities in Aspen Parkland rangeland using LiDAR data. Applied Vegetation Science 10, 407–416.

Vauhkonen, J., Korpela, I., Maltamo, M., Tokola, T., 2010. Imputation of single-tree attributes using airborne laser scanning-based height, intensity, and alpha shape metrics. Remote Sensing of Environment 114, 1263–1276.

Weiskittel, A., Russell, M., Wagner, R., Seymour, R., 2012. Refinement of the Forest Vegetation Simulator Northeast variant growth and yield model: Phase III. In B. Roth (ed.), Cooperative Forest Research Unit. Orono, ME: University of Maine, School of Forest Resources. 96–104.

White, J.C., Wulder, M.A., Varhola, A., Vastaranta, M., Coops, N., Cook, B.D., Pitt, D., Woods, M., 2013. A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach. Information Report FI-X-010. Canadian Forest Service, Canadian Wood Fibre Centre. Victoria, British Columbia.

Wood, G.B., Wiant, H.V., Loy, R.J., Miles, J.A., 1990. Centroid sampling: A variant of importance sampling for estimating the volume of sample trees of radiata pine. Forest Ecology and Management 36, 233–243.

Yu, X., Hyyppä, J., Holopainen, M., Vastaranta, M., 2010. Comparison of area-based and individual tree-based methods for predicting plot-level forest attributes. Remote Sensing 2, 1481–1495.


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