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

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. R 2 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.

[1]  K. Lim,et al.  Lidar remote sensing of biophysical properties of tolerant northern hardwood forests , 2003 .

[2]  S. Popescu,et al.  Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomass , 2003 .

[3]  D. Donoghue,et al.  Remote sensing of species mixtures in conifer plantations using LiDAR height and intensity data , 2007 .

[4]  J. Reitberger,et al.  Analysis of full waveform LIDAR data for the classification of deciduous and coniferous trees , 2008 .

[5]  P. Treitz,et al.  Mapping stand-level forest biophysical variables for a mixedwood boreal forest using lidar: an examination of scanning density , 2006 .

[6]  Thomas P. Ryan,et al.  Modern Regression Methods , 1996 .

[7]  The influence of post-spacing density of DEMS derived from LIDAR on flood modeling , 2004 .

[8]  D. A. Crouse,et al.  Horizontal resolution and data density effects on remotely sensed LIDAR-based DEM , 2006 .

[9]  Ra Musk,et al.  Calibrating LiDAR derived canopy metrics to account for data aquisition parameters and forest condition in Radiata pine plantations , 2007 .

[10]  J. Hyyppä,et al.  Estimation of stem volume using laser scanning-based canopy height metrics , 2006 .

[11]  Daniel Peña Sánchez de Rivera.  Regresión y diseño de experimentos , 2002 .

[12]  Randolph H. Wynne,et al.  Estimating forest biomass using small footprint LiDAR data: An individual tree-based approach that incorporates training data , 2005 .

[13]  I. Burke,et al.  Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests , 2005 .

[14]  J. V. van Aardt,et al.  Estimating plot-level tree height and volume of Eucalyptus grandis plantations using small-footprint, discrete return lidar data , 2010 .

[15]  E. Næsset.  Estimating timber volume of forest stands using airborne laser scanner data , 1997 .

[16]  R. H. Myers.  Classical and modern regression with applications , 1986 .

[17]  N. Draper,et al.  Applied Regression Analysis: Draper/Applied Regression Analysis , 1998 .

[18]  S. Popescu.  Estimating biomass of individual pine trees using airborne lidar , 2007 .

[19]  R. Chris Olsen,et al.  Effects of lidar point density on bare earth extraction and DEM creation , 2009, Defense + Commercial Sensing.

[20]  John B. Vogler,et al.  ESTIMATION OF LAND-USE IN AN URBANIZED LANDSCAPE USING LIDAR INTENSITY DATA: A REGIONAL SCALE APPROACH , 2010 .

[21]  Javier Estornell,et al.  Estimation of shrub biomass by airborne LiDAR data in small forest stands , 2011 .

[22]  Markus Hollaus,et al.  Airborne Laser Scanning of Forest Stem Volume in a Mountainous Environment , 2007, Sensors (Basel, Switzerland).

[23]  W. Wagner,et al.  3D vegetation mapping using small‐footprint full‐waveform airborne laser scanners , 2008 .

[24]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[25]  E. Næsset.  Accuracy of forest inventory using airborne laser scanning: evaluating the first nordic full-scale operational project , 2004 .

[26]  E. Næsset,et al.  Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser , 2008 .

[27]  R. Nelson,et al.  Regional aboveground forest biomass using airborne and spaceborne LiDAR in Québec. , 2008 .

[28]  Mattias Magnusson,et al.  Evaluation of remote sensing techniques for estimation of forest variables at stand level , 2006 .

[29]  Johan E. S. Fransson,et al.  Effects on estimation accuracy of forest variables using different pulse density of laser data , 2007 .

[30]  E. Næsset,et al.  ASSESSING EFFECTS OF LASER POINT DENSITY ON BIOPHYSICAL STAND PROPERTIES DERIVED FROM AIRBORNE LASER SCANNER DATA IN MATURE FOREST , 2007 .

[31]  J. Rosette,et al.  Variability of LiDAR volume prediction models for productivity assessment of radiata pine plantations in South Australia , 2008 .

[32]  M. Hodgson,et al.  An evaluation of LIDAR- and IFSAR-derived digital elevation models in leaf-on conditions with USGS Level 1 and Level 2 DEMs , 2003 .

[33]  William Kruskal,et al.  A Nonparametric test for the Several Sample Problem , 1952 .

[34]  Nicholas C. Coops,et al.  Assessment of forest structure with airborne LiDAR and the effects of platform altitude , 2006 .

[35]  Eduardo González-Ferreiro,et al.  Assessing the attributes of high-density Eucalyptus globulus stands using airborne laser scanner data , 2011 .

[36]  E. Næsset.  Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data , 2002 .

[37]  Michael A. Lefsky,et al.  Forest structure estimation and pattern exploration from discrete-return lidar in subalpine forests of the central Rockies , 2007 .

[38]  W. Kruskal,et al.  Use of Ranks in One-Criterion Variance Analysis , 1952 .

[39]  N. Draper,et al.  Applied Regression Analysis , 1966 .

[40]  R. Lucas,et al.  A review of remote sensing technology in support of the Kyoto Protocol , 2003 .

[41]  M. Flood,et al.  LiDAR remote sensing of forest structure , 2003 .

[42]  K. Lim,et al.  Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators , 2004 .

[43]  K. Kraus,et al.  Determination of terrain models in wooded areas with airborne laser scanner data , 1998 .

[44]  T. Dawson,et al.  Quantifying forest above ground carbon content using LiDAR remote sensing , 2004 .

[45]  C. Pérez-Cruzado,et al.  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 , 2011 .

[46]  J. A. Tullis,et al.  An Evaluation of Lidar-derived Elevation and Terrain Slope in Leaf-off Conditions , 2005 .

[47]  David A. Belsley,et al.  Conditioning Diagnostics: Collinearity and Weak Data in Regression , 1991 .

[48]  Dafydd Gibbon,et al.  1 User’s guide , 1998 .

[49]  Linear Least-Squares Interpolation , 2008 .

[50]  R. Wack,et al.  FOREST INVENTORY FOR EUCALYPTUS PLANTATIONS BASED ON AIRBORNE LASERSCANNER DATA , 2003 .

[51]  Unfccc.  Kyoto Protocol to the United Nations Framework Convention on Climate Change , 1997 .

[52]  M. B. Wilk,et al.  Approximations for the Null Distribution of the W Statistic , 1968 .

[53]  R. Dubayah,et al.  Above-ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: factors affecting the generality of relationships , 2003 .

[54]  N. Pfeifer,et al.  Correction of laser scanning intensity data: Data and model-driven approaches , 2007 .

[55]  M. Favalli,et al.  Lava flow identification and aging by means of lidar intensity: Mount Etna case , 2007 .

[56]  S. Shapiro,et al.  An Analysis of Variance Test for Normality (Complete Samples) , 1965 .

[57]  Zhenyu Zhang,et al.  LIDAR data reduction for efficient and high quality DEM generation , 2008 .

[58]  Nicholas C. Coops,et al.  Simulation study for finding optimal lidar acquisition parameters for forest height retrieval , 2005 .

[59]  E. Næsset,et al.  Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve , 2002 .

[60]  Michela Robba,et al.  Optimizing forest biomass exploitation for energy supply at a regional level , 2004 .

[61]  Boris Jutzi,et al.  Intensity Normalization by Incidence Angle and Range of Full-Waveform LIDAR Data , 2008 .

[62]  David J. Sheskin,et al.  Handbook of Parametric and Nonparametric Statistical Procedures , 1997 .

[63]  M. Heurich,et al.  Estimation of forestry stand parameters using laser scanning data in temperate, structurally rich natural European beech (Fagus sylvatica) and Norway spruce (Picea abies) forests , 2008 .

[64]  E. Næsset.  Practical large-scale forest stand inventory using a small-footprint airborne scanning laser , 2004 .

[65]  E. Næsset,et al.  Estimating tree heights and number of stems in young forest stands using airborne laser scanner data , 2001 .

[66]  C. Field,et al.  Biomass energy: the scale of the potential resource. , 2008, Trends in ecology & evolution.

[67]  K. Rennolls,et al.  Timber Management-A Quantitative Approach. , 1984 .

[68]  J. R. Jensen,et al.  Creation of digital terrain models using an adaptive lidar vegetation point removal process , 2002 .

[69]  Michael E. Hodgson,et al.  Impact of Lidar Nominal Post-spacing on DEM Accuracy and Flood Zone Delineation , 2007 .

[70]  F. M. Danson,et al.  Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data , 2010 .

[71]  Eduardo González-Ferreiro,et al.  Estimation of stand variables in Pinus radiata D. Don plantations using different LiDAR pulse densities , 2012 .

[72]  J. Hyyppä,et al.  Review of methods of small‐footprint airborne laser scanning for extracting forest inventory data in boreal forests , 2008 .

[73]  Robert J. McGaughey,et al.  FOREST MEASUREMENT AND MONITORING USING HIGH-RESOLUTION AIRBORNE LIDAR , 2006 .

[74]  Boris Jutzi,et al.  Investigations on surface reflection models for intensity normalization in airborne laser scanning (ALS) data , 2010 .