Impact of plot size and model selection on forest biomass estimation using airborne LiDAR: A case study of pine plantations in southern Spain R.M., González-Ferreiro E., García-Gutiérrez J., Ceacero Ruiz C.J., Hernández-Clemente R. (2017): Impact of plot size and model selection on forest biomass estimation using airborne LiDAR: A case study of pine plantations in southern Spain. J. For. Sci., 63: 88-97.
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We explored the usefulness of LiDAR for modelling and mapping the stand biomass of two conifer species in southern Spain. We used three different plot sizes and two statistical approaches (i.e. stepwise selection and genetic algorithm selection) in combination with multiple linear regression models to estimate biomass. 43 predictor variables derived from discrete-return LiDAR data (4 pulses per m2) were used for estimating the forest biomass of Pinus sylvestris Linnaeus and Pinus nigra Arnold forests. Twelve circular plots – six for each species – and three different fixed-radius designs (i.e. 7, 15, and 30 m) were established within the range of the airborne LiDAR. The Bayesian information criterion and R2 were used to select the best models. As expected, the models that included the largest plots (30 m) yielded the highest R2 value (0.91) for Pinus sp. using genetic algorithm models. Considering P. sylvestris and P. nigra models separately, the genetic algorithm approach also yielded the highest R2 values for the 30-m plots (P. nigra: R2 = 0.99, P. sylvestris: R2 = 0.97). The results we obtained with two species and different plot sizes revealed that increasing the size of plots from 15 to 30 m had a low effect on modelling attempts.

Anderson Jeanne, Martin M.E., Smith M-L., Dubayah R.O., Hofton M.A., Hyde P., Peterson B.E., Blair J.B., Knox R.G. (2006): The use of waveform lidar to measure northern temperate mixed conifer and deciduous forest structure in New Hampshire. Remote Sensing of Environment, 105, 248-261
Avery T.E., Burkhart H. (1994): Forest Measurements. 4th Ed. Boston, McGraw-Hill: 407.
Belsley D. (1991): Conditioning Diagnostics: Collinearity and Weak Data in Regression. New York, John Wiley & Sons: 396.
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
Garcia-Gutierrez Jorge, Gonzalez-Ferreiro Eduardo, Riquelme-Santos Jose C., Miranda David, Dieguez-Aranda Ulises, Navarro-Cerrillo Rafael M. (2014): Evolutionary feature selection to estimate forest stand variables using LiDAR. International Journal of Applied Earth Observation and Geoinformation, 26, 119-131
Gobakken Terje, Næsset Erik (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
Gonzalez-Ferreiro E., Dieguez-Aranda U., Miranda D. (): Estimation of stand variables in Pinus radiata D. Don plantations using different LiDAR pulse densities. Forestry, 85, 281-292
González-Ferreiro Eduardo, Diéguez-Aranda Ulises, Barreiro-Fernández Laura, Buján Sandra, Barbosa Miguel, Suárez Juan C., Bye Iain J., Miranda David (2013): A mixed pixel- and region-based approach for using airborne laser scanning data for individual tree crown delineation in Pinus radiata D. Don plantations. International Journal of Remote Sensing, 34, 7671-7690
Hernández-Clemente Rocío, Navarro-Cerrillo Rafael M., Suárez Lola, Morales Fermín, Zarco-Tejada Pablo J. (2011): Assessing structural effects on PRI for stress detection in conifer forests. Remote Sensing of Environment, 115, 2360-2375
Latifi H., Nothdurft A., Koch B. (): 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., 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.
Maltamo M (2004): Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions. Remote Sensing of Environment, 90, 319-330
Mauro F., Valbuena R., García A., Manzanera J. (2009): GPS admissible errors in positioning inventory plots for forest structure studies. Available at
McGaughey R. (2009): FUSION/LDV: Software for LIDAR Data Analysis and Visualization. Seattle, USDA Forest Service, Pacific Northwest Research Station: 182.
Means J., Acker S., Fitt B., Renslow M., Emerson L. (2000): Predicting forest stand characteristics with airborne scanning LIDAR. Photogrammetric Engineering and Remote Sensing, 66: 1367–1371.
Næsset Erik (2002): Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sensing of Environment, 80, 88-99
Næsset Erik (2004): Accuracy of forest inventory using airborne laser scanning: evaluating the first nordic full-scale operational project. Scandinavian Journal of Forest Research, 19, 554-557
Navarro-Cerrillo Rafael Mª, Trujillo Jesus, de la Orden Manuel Sánchez, Hernández-Clemente Rocío (2014): Hyperspectral and multispectral satellite sensors for mapping chlorophyll content in a Mediterranean Pinus sylvestris L. plantation. International Journal of Applied Earth Observation and Geoinformation, 26, 88-96
Nelson Ross, Short Austin, Valenti Michael (2004): Measuring biomass and carbon in delaware using an airborne profiling LIDAR. Scandinavian Journal of Forest Research, 19, 500-511
Renner Gábor, Ekárt Anikó (2003): Genetic algorithms in computer aided design. Computer-Aided Design, 35, 709-726
Ruiz-Peinado Ricardo, Del Rio Miren, Montero Gregorio (2011): New models for estimating the carbon sink capacity of Spanish softwood species. Forest Systems, 20, 176-
Schwarz Gideon (1978): Estimating the Dimension of a Model. The Annals of Statistics, 6, 461-464
Stevens J. (2002): Applied Multivariate Statistics for the Social Sciences. Hillsdale, Lawrence Erlbaum Associates, Inc.: 699.
Sun Guoqing, Ranson K. Jon, Guo Z., Zhang Z., Montesano P., Kimes D. (2011): Forest biomass mapping from lidar and radar synergies. Remote Sensing of Environment, 115, 2906-2916
Whittingham M.J., Stephens P.A., Bradbury R.B., Freckleton R. (2006): Why do we still use stepwise modelling in ecology and behaviour? Journal of Animal Ecology, 75: 1182–1189.
Zenner Eric K. (2005): Investigating scale-dependent stand heterogeneity with structure-area-curves. Forest Ecology and Management, 209, 87-100
Zhao Kaiguang, Popescu Sorin (2009): Lidar-based mapping of leaf area index and its use for validating GLOBCARBON satellite LAI product in a temperate forest of the southern USA. Remote Sensing of Environment, 113, 1628-1645
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