Investigating laser pulse penetration through a conifer canopy by integrating airborne and terrestrial lidar

This study examines the distribution of laser pulse returns obtained from coincident airborne and terrestrial lidar surveys of a closed-canopy red pine (Pinus resinosa) plantation. The purpose of this study is to improve our understanding of laser pulse sampling within closed canopies so that estimates of forest structural variables (e.g., biomass, needle-leaf area, and base-of-live-crown) can be improved at the individual tree and stand levels using lidar. The results of this study indicate the following: (1) There is a statistically significant difference between field measurements of tree height and estimates derived from the maximum laser pulse return from airborne and terrestrial lidar. In both cases, maximum laser pulse returns underestimate tree height by 1 m, on average. (2) Both terrestrial and airborne lidar are unable to discern the base of the measured live crown. Laser pulse returns from airborne lidar are biased towards the top of the tree crown, i.e., lowest laser pulse returns occur 1.4 m on average higher in the canopy than the measured base-of-live-crown. On the other hand, terrestrial lidar captures dieback at the base of the live crown, thereby lowering the base-of-live-crown estimate by 6.6 m, on average. (3) Median airborne laser pulse returns within the canopy (20.4 m), believed to be associated with needle leaf area, occur below the maximum frequency of laser pulse returns (20.8 m) but higher in the canopy than the height of maximum crown diameter obtained from terrestrial lidar (18.0 m). The bias of airborne laser pulse reflections towards the top of the canopy with less penetration to a depth where the maximum crown diameter occurs may result in an underestimation of the needle leaf area. The results of this research suggest that future research should focus on improving our understanding of how laser pulse returns are "triggered" within vegetated environments and how canopy properties or data acquisition parameters may influence the location of this "trigger" event.

[1]  H. Spiecker,et al.  EVALUATION AND FUTURE PROSPECTS OF TERRESTRIAL LASER SCANNING FOR STANDARDIZED FOREST INVENTORIES , 2004 .

[2]  N. Pfeifer,et al.  Modelling of Tree Cross Sections from Terrestrial Laser Scanning Data with Free-form Curves , 2004 .

[3]  J. Means,et al.  Predicting forest stand characteristics with airborne scanning lidar , 2000 .

[4]  Erik Næsset,et al.  Assessing sensor effects and effects of leaf-off and leaf-on canopy conditions on biophysical stand properties derived from small-footprint airborne laser data , 2005 .

[5]  Laura Chasmer,et al.  Towards a universal lidar canopy height indicator , 2006 .

[6]  Juha Hyyppä,et al.  The accuracy of estimating individual tree variables with airborne laser scanning in a boreal nature reserve , 2004 .

[7]  J. Holmgrena,et al.  Large Scale Airborne Laser Scanning of Forest Resources in Sweden , 2004 .

[8]  R. Hill,et al.  Quantifying canopy height underestimation by laser pulse penetration in small-footprint airborne laser scanning data , 2003 .

[9]  G. A. Blackburn,et al.  Mapping individual tree location, height and species in broadleaved deciduous forest using airborne LIDAR and multi‐spectral remotely sensed data , 2005 .

[10]  C. Brack,et al.  QUANTIFIYING VERTICAL FOREST STAND STRUCTURE USING SMALL FOOTPRINT LIDAR TO ASSESS POTENTIAL STAND DYNAMICS . , 2004 .

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

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

[13]  David J. Harding,et al.  Light transmittance in forest canopies determined using airborne laser altimetry and in-canopy quantum measurements , 2001 .

[14]  Ben Gorte,et al.  RECONSTRUCTION OF LASER-SCANNED TREES USING FILTER OPERATIONS IN THE 3D RASTER DOMAIN , 2004 .

[15]  H. Spiecker,et al.  APPROACHES FOR RECOGNITION OF WOOD QUALITY OF STANDING TREES BASED ON TERRESTRIAL LASERSCANNER DATA , 2004 .

[16]  Laura Chasmer,et al.  Vegetation class dependent errors in lidar ground elevation and canopy height estimates in a boreal wetland environment , 2005 .

[17]  N. Coops,et al.  Using airborne and ground-based ranging lidar to measure canopy structure in Australian forests , 2003 .

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

[19]  Michael A. Lefsky,et al.  Combining lidar estimates of aboveground biomass and Landsat estimates of stand age for spatially extensive validation of modeled forest productivity , 2005 .

[20]  David Ebdon,et al.  Statistics in geography , 1986 .

[21]  Pol Coppin,et al.  Reconstruction of tree structure from laser-scans and their use to predict physiological properties and processes in canopies , 2004 .

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

[23]  C. Hopkinson,et al.  Assessing forest metrics with a ground-based scanning lidar , 2004 .

[24]  S. Magnussen,et al.  Derivations of stand heights from airborne laser scanner data with canopy-based quantile estimators , 1998 .