Modelling stand biomass fractions in Galician Eucalyptus globulus plantations by use of different LiDAR pulse densities

E.M. González-Ferreiro, D. Miranda, L. Barreiro-Fernandez, S. Bujan, J. Garcia-Gutierrez, U. Dieguez-Aranda


Aims of study: To evaluate the potential use of canopy height and intensity distributions, determined by airborne LiDAR, for the estimation of crown, stem and aboveground biomass fractions.

To assess the effects of a reduction in LiDAR pulse densities on model precision.

Area of study: The study area is located in Galicia, NW Spain. The forests are representative of Eucalyptus globules stands in NW Spain, characterized by low-intensity silvicultural treatments and by the presence of tall shrub.

Material and methods: Linear, multiplicative power and exponential models were used to establish empirical relationships between field measurements and LiDAR metrics.

A random selection of LiDAR returns and a comparison of the prediction errors by LiDAR pulse density factor were performed to study a possible loss of fit in these models.

Main results: Models showed similar goodness-of-fit statistics to those reported in the international literature. R2 ranged from 0.52 to 0.75 for stand crown biomass, from 0.64 to 0.87 for stand stem biomass, and from 0.63 to 0.86 for stand aboveground biomass. The RMSE/MEAN · 100 of the set of fitted models ranged from 17.4% to 28.4%.

Models precision was essentially maintained when 87.5% of the original point cloud was reduced, i.e. a reduction from 4 pulses m–2 to 0.5 pulses m–2.

Research highlights: Considering the results of this study, the low-density LiDAR data that are released by the Spanish National Geographic Institute will be an excellent source of information for reducing the cost of forest inventories.

Key words: Eucalypt plantations; airborne laser scanning; aboveground biomass; carbon stocks; remote sensing; forest inventory.

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Andersen HE, McGaughey RJ, Reutebuch SE, 2005. Forest measurement and monitoring using high-resolution airborne LIDAR. In: Productivity of western forests: a forest products focus (Harrington CA, Schoenholtz SH, eds). Department of Agriculture, Forest Service, Pacific Northwest Research Station, Portland, USA. pp: 109-120.

Anderson E, Thompson J, Crouse D, Austin R, 2006. Horizontal resolution and data density effects on remotely sensed LIDAR-based DEM. Geoderm 132: 406-415.

Belsley D, 1991. Conditioning diagnostics: collinearity and weak data in regression, first ed. John Wiley & Sons, Inc, New York, USA. 396 pp.

Bortolot ZJ, Wynne RH, 2005. Estimating forest biomass using small footprint LiDAR data: an individual treebased approach that incorporates training data. ISPRS J Photogramm Remote Sens 59: 342-360.

Boudreau J, Nelson RF, Margolis HA, Beaudoin A, Guindon L, Kimes DS, 2008. Regional aboveground forest biomass using airborne and spaceborne LiDAR in Quebec. Remote Sens Environ 112: 3876-3890.

Clutter JL, Forston JC, Pienaar LV, Brister GH, Bailey RL, 1983. Timber management: a quantitative approach, first ed. John Wiley & Sons, Inc, New York, USA. 333 pp.

Confemadera, 2010. Informe de resultados: industria de la madera de Galicia, 2010 [online]. Available in: 0de%20resultados2010OK.pdf [19 Dec 2012].

Diéguez-Aranda U, Rojo Alboreca A, Castedo-Dorado F, Álvarez González JG, Barrio-Anta M, Crecente-Campo F, González González JM, Pérez-Cruzado C, Rodríguez Soalleiro R, López-Sánchez CA, Balboa-Murias MA, Gorgoso Varela JJ, Sánchez Rodríguez F, 2009. Herramientas selvícolas para la gestión forestal sostenible en Galicia, first ed. Xunta de Galicia, Santiago de Compostela, Spain. 259 pp.

Donoghue DNM, Watt PJ, Cox NJ, Wilson J, 2007. Remote sensing of species mixtures in conifer plantations using LiDAR height and intensity data. Remote Sens Environ 110: 509-522.

Drake JB, Knox RG, Dubayah RO, Clark DB, Condit R, Blair JB, Hofton M, 2003. Above-ground biomass estimation in closed canopy neotropical forests using lidar remote sensing: factors affecting the generality of relationships. Global Ecol Biogeogr 12: 147-159.

Draper NR, Smith H, 1998. Applied regression analysis, 3rd ed. John Wiley & Sons, Inc, New York, USA. 736 pp.

ESRI, 1998. ESRI shapefile technical description. White paper, first ed [online]. Environmental Systems Research Institute, Inc, Redlands, USA. 34 pp. Available in: [19 Dec 2012].

Estornell J, Ruiz L, Velázquez-Martí B, Fernández-Sarría A, 2011. Estimation of shrub biomass by airborne LiDAR data in small forest stands. For Ecol Manage 262: 1697- 1703.

Field CB, Campbell JE, Lobell DB, 2008. Biomass energy: the scale of the potential resource. Trends Ecol Evol 23: 65-72. PMid:18215439

Freppaz F, Minciardi R, Robba M, Rovatti M, Sacile R, Taramasso A, 2004. Optimizing forest biomass exploitation for energy supply at a regional level. Biomass Energy 26: 15-25.

Garcia M, Fiano D, Chuvieco E, Danson FM, 2010. Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data. Remote Sens Environ 114: 816-830.

Gobakken T, Næsset E, 2007. Assessing effects of laser point density on biophysical stand properties derived from airborne laser scanner data in mature forest. Int Arch Photogram Rem Sens Spatial Inform Sci 36: 150-155.

Gonçalves-Seco L, González-Ferreiro E, Diéguez-Aranda U, Fraga-Bugallo B, Crecente R, Miranda D, 2011. Assessing attributes of high density Eucalyptus globulus stands using Airborne Laser Scanner data. Int J Remote Sens 32: 9821-9841.

González-Ferreiro E, Diéguez-Aranda U, Miranda D, 2012. Estimation of stand variables in Pinus radiata D. Don plantations using different LiDAR pulse densities. Forestry 85: 281-292

Goodwin NR, Coops NC, Culvenor DS, 2006. Assessment of forest structure with airborne LiDAR and the effects of platform altitude. Remote Sens Environ 103: 140-152.

Gross H, Jutzi B, Thoennessen U, 2008. Intensity normalization by incidence angle and range of full-waveform lidar data. Int Arch Photogram Rem Sens Spatial Inform Sci 37: 405-412.

Gueudet P, 2004. The influence of post spacing density of DEMs derived from LIDAR on flood modelling. Doctoral thesis. University of Texas at Austin.

Hall S, Burke I, Box D, Kaufmann M, Stoker J, 2005. Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests. For Ecol Manage 208: 189-209.

Heurich M, Thoma F, 2008. Estimation of forestry stand parameters using laser scanning data in temperate, structurally rich natural European beech (Fagus sylvatica) and Norway spruce (Picea abies) forests. Forestry 81: 645-661.

Hodgson ME, Jensen JR, Schmidt L, Schill S, Davis B, 2003. An evaluation of LIDAR- and IFSAR-derived digital elevation models in leaf-on conditions with USGS Level 1 and Level 2 DEMs. Remote Sens Environ 84: 295-308.

Hodgson ME, Jensen J, Raber G, Tulils J, Davis BA, Thompson G, Schuckman K, 2005. An evaluation of LIDAR-derived elevations and terrain slope in leaf-off conditions. Photogramm Eng Remote Sens 71: 817-23.

Höfle B, Pfeifer N, 2007. Correction of laser scanning intensity data: data and model-driven approaches. ISPRS J Photogramm Remote Sens 62: 1415-1433.

Hollaus M, Wagner W, Maier B, Schadauer K, 2007. Airborne laser scanning of forest stem volume in a mountainous environment. Sensors 7: 1559-1577.

Hyyppä J, Hyyppä H, Leckie D, Gougeon F, Yu X, Maltamo M, 2008. Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests. Int J Remote Sens 29: 1339-1366.

Jutzi B, Gross H, 2010. Investigation on surface reflection models for intensity normalization in Airborne Laser Scanning (ALS) data. Photogramm Eng Remote Sens 76: 1051-1060.

Kraus K, Mikhail EM, 1972. Linear least squares interpolation. Photogramm Eng 38: 1016-1029.

Kraus K, Pfeifer N, 1998. Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS J Photogramm Remote Sens 53: 193-203.

Kruskal WH, 1952. A nonparametric test for the several sample problem. Ann Math Stat 23: 525-540.

Kruskal WH, Wallis WA, 1952. Use of ranks in one-criterion variance analysis. J Am Stat Assoc 47: 583-621.

Lim KS, Treitz PM, 2004a. Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators. Scand J Forest Res 19: 558-570.

Lim KS, Treitz PM, 2004b. Estimation of aboveground forest biomass using airborne scanning discrete return LIDAR in Douglas-fir. Int Arch Photogram Rem Sens Spatial Inform Sci 36: 149-152.

Lim K, Treitz P, Baldwin K, Morrison I, Green J, 2003a. Lidar remote sensing of biophysical properties of toleran northern hardwood forests. Can J Remote Sens 29: 658-678.

Lim K, Treitz P, Wulder M, St-Onge B, Flood M, 2003b. LiDAR remote sensing of forest structure. Progress in Physical Geography 27: 88-106.

Liu X, Zhang Z, 2008. Lidar data reduction for efficient and high quality dem generation. Int Arch Photogram Rem Sens Spatial Inform Sci 37: 173-178.

Lovell JL, Jupp DLB, Newnham GJ, Coops NC, Culvenor DS, 2005. Simulation study for finding optimal lidar acquisition parameters for forest height retrieval. For Ecol Manage 214: 398-412.

Magnusson M, 2006. Evaluation of remote sensing techniques for estimation of forest variables at stand level. Doctoral thesis. Swedish University of Agricultural Sciences, Umeå.

Magnusson M, Fransson J, Holmgren J, 2007. Effects on estimation accuracy of forest variables using different pulse density of laser data. For Sci 53: 619-626.

Maltamo M, Eerikäinen K, Packalén P, Hyyppä J, 2006. Estimation of stem volume using laser scanning-based canopy height metrics. Forestry 79: 217-229.

Mazzarini F, Pareschi MT, Favalli M, Isola I, Tarquini S, Boschi E, 2007. Lava flow identification and aging by means of lidar intensity: mount Etna case. J Geoph Research 112.

McGaughey R, 2009. FUSION/LDV: software for LIDAR Data Analysis and Visualization, US Department of Agriculture, Forest Service, Pacific Northwest Research Station, Seattle, USA. 123 pp.

Musk RA, Osborn JE, 2007. Calibrating LiDAR derived canopy metrics to account for data aquisition parameters and forest condition in Radiata pine plantations [online]. In: School of Geography and Environmental Studies Conference, Hobart, Tasmania. pp: 1-6. Available in: [19 Dec 2012].

Myers RH, 1990. Classical and modern regression with applications, 2nd ed. Duxbury Press, Belmont, CA, USA. 488 pp.

Næsset E, 1997. Estimating timber volume of forest stands using airborne laser scanner data. Remote Sens Environ 61: 246-253.

Næsset E, 2002. Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sens Environ 80: 88-99.

Næsset E, 2004. Practical large-scale forest stand inventory using a small-footprint airborne scanning laser. Scand J Forest Res 19: 164-179.

Næsset E, Økland T, 2002. Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve. Remote Sens Environ 79: 105-115.

Næsset E, Bjerknes K, 2001. Estimating tree heights and number of stems in young forest stands using airborne laser scanner data. Remote Sens Environ 78: 328-340.

Næsset E, Gobakken T, 2008. Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser. Remote Sens Environ 112: 3079-3090.

Patenaude G, Hill RA, Milne R, Gaveau DLA, Briggs BBJ, Dawson TP, 2004. Quantifying forest above ground carbon content using LiDAR remote sensing. Remote Sens Environ 93: 368-380.

Peña D, 2002. Regresión y dise-o de experimentos. Alianza Editorial SA, Madrid, España. 744 pp.

Pérez-Cruzado C, Merino A, Rodríguez-Soalleiro R, 2011. A management tool for estimating bioenergy production and carbon sequestration in Eucalyptus globulus and Eucalyptus nitens grown as short rotation woody crops in north-west Spain. Biomass and Bioenergy 35: 2839-2851.

Popescu SC, 2007. Estimating biomass of individual pine trees using airborne Lidar. Biomass and Bioenergy 31: 646-655.

Popescu SC, Wynne RH, Nelson RF, 2003. Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomass. Can J Remote Sens 29: 564-577.

Puetz A, Olsen R, Anderson B, 2009. Effects of lidar point density on bare earth extraction and DEM creation. In: Laser radar technology and applications (Turner MD, Kamerman GW, eds) . XIV Proceedings of the SPIE 7323. pp: 1-8.

Raber GT, Jensen JR, Schill SR, Schuckman L, 2002. Creation of digital terrain models using an adaptive lidar vegetation point removal process. Photogram Eng Remote Sens 68: 1307-1315.

Raber G, Jensen J, Hodgson M, Tullis J, Davis B, Berglund J, 2007. Impact of lidar nominal post-spacing on dem accuracy and flood zone delineation. Photogramm Eng Remote Sens 73: 793-804.

Reitberger J, Krzystek P, Stilla U, 2008. Analysis of full waveform LIDAR data for the classification of deciduous and coniferous trees. Int J Remote Sens 29: 1407-1431.

Rombouts J, Ferguson IS, Leech JW, 2008. Variability of LiDAR volume prediction models for productivity assessment of radiata pine plantations in South Australia (Hill RA, Rosette J, Suárez J, eds). In: Proceedings of Silvilaser 2008: 8th international conference on LiDAR applications in forest assessment and inventory Edinburgh, UK. pp: 39-49.

Rosenqvist A, Milne A, Lucas R, Imhoff M, Dobson C, 2003. A review of remote sensing technology in support of the Kyoto Protocol. Environ Sci Policy 6: 441-455.

Ryan TP, 1997. Modern Regression Methods, first ed. John Wiley & Sons, Inc, New York, USA. 515 pp.

SAS Institute Inc, 2004. SAS/STAT® 9.1 User's Guide. SAS Institute Inc, Cary, NC, USA.

Schwarz G, 1978. Estimating the dimension of a model. Ann Stat 6: 461-464.

Shapiro SS, Wilk MB, 1965. An analysis of variance test for normality (complete samples). Biometrika 52: 591-611.

Shapiro SS, Wilk MB, 1968. Approximations for the null distribution of the W statistic. Technometrics 10: 861-866.

Sherrill KR, Lefsky MA, Bradford JB, Ryan MG, 2008. Forest structure estimation and pattern exploration from discrete-return lidar in subalpine forests of the central rockies. Can J For Res 38: 2081-2096.

Sheskin DJ, 2004. Handbook of parametric and nonparametric statistical procedures, 3rd ed. Chapman & Hall/CRC, Boca Ratón, FL, USA. 1193 pp.

Silva-Pando FJ, González-Hernández MP, Prunell Tuduri A, 1993. Prácticas agroforestales en pinares y eucaliptales atlánticos. I. Producción del sotobosque II (Silva-Pando, FJ, Vega G, eds). In: Actas del I Congreso Forestal Espa-ol (Lourizán, España). pp: 637-642.

Singh KK, Vogler JB, Meentemeyer RK, 2010. Estimation of land-use in an urbanized landscape using lidar intensity data: a regional scale approach. Int Arch Photogram Rem Sens Spatial Inform Sci 38; 1-4.

Tesfamichael SG, Van Aardt JAN, Ahmed F, 2010. Estimating plot-level tree height and volume of Eucalyptus grandis plantations using small-footprint, discrete return lidar data. Prog Phys Geog 34: 515-540.

Thomas V, Treitz P, McCaughey JH, Morrison I, 2006. Mapping stand-level forest biophysical variables for a mixedwood boreal forest using lidar: an examination of scanning density. Can J For Res 36: 34-47.

Treitz P, Kevin L, Murray W, Doug P, Nesbitt D, Etheridge D, 2010. LiDAR data acquisition and processing protocols for forest resource inventories in Ontario, Canada (Koch B, Kendlar G, eds). In: Silvilaser 2010: the 10th International Conference on LiDAR Applications for Assessing Forest Ecosystems Freiburg, Germany. pp: 1-10.

UNFCCC, 1997. Kyoto Protocol to the United Nation Framework Convention on Climate Change [online]. Available in: [19 Dec 2012].

Wack R, Schardt M, Lohr U, Barrucho L, Oliveira T, 2003. Forest inventory for Eucalyptus plantations based on airborne laser scanner data. Int Arch Photogram Rem Sens Spatial Inform Sci 34: 40-46.

Wagner W, Hollaus M, Briese C, Ducic V, 2008. 3D vegetation mapping using small-footprint full-waveform airborne laser scanners. Int J Remote Sens 29: 1433- 1452.

DOI: 10.5424/fs/2013223-03878