Effects of voxel size and sampling setup on the estimation of forest canopy gap fraction from terrestrial laser scanning data

Abstract Assessments of forest canopy structure remain a challenge and are most often conducted using indirect techniques limited to a two-dimensional perspective. Using terrestrial laser scanner (TLS) technology, a three-dimensional (3D) approach to study canopy structure was conducted by modeling forest scenes from three broadleaved forest stands with different canopy features. Field TLS data were collected from each stand using a phase based FARO® LS880 laser scanner on four sampling setups. The capability of TLS-derived data to represent canopy structure was evaluated by comparing gap fraction estimates from the 3D models with gap fraction values from digital hemispherical photographs (DHP). Firstly, the collected 3D point clouds were processed to obtain fully representative voxel-based models of the forest canopy. Secondly, ray tracing algorithms were applied on these models to simulate hemispherical views and estimate gap fraction. Finally, a sensitivity analysis was done using different voxel sizes and the four sampling setups on the simulations, in order to assess their impact on the gap fraction estimates derived from TLS data. Results of TLS-derived gap fraction showed that combining nine scans produced better results in all forest stand. Similarly, the dimension of voxels have a marked influence on these results. Voxel sizes of 1 cm, 2 cm and 4 cm were found to have less error when compared with real gap fraction values derived from DHP for young, intermediate and mature forest stands, respectively (RMSE ranging from 9% to 16%). However, substantial differences in gap fraction were observed and described at different zenith angles. These results suggest that specific TLS sampling setup and processing are required depending upon the forest type under analysis. Overall, this research indicates that phase based TLS data can be used for objective calculation of gap fraction.

[1]  Frédéric Baret,et al.  Review of methods for in situ leaf area index determination Part I. Theories, sensors and hemispherical photography , 2004 .

[2]  F. Baret,et al.  Review of methods for in situ leaf area index (LAI) determination: Part II. Estimation of LAI, errors and sampling , 2004 .

[3]  C. Woodcock,et al.  Measuring forest structure and biomass in New England forest stands using Echidna ground-based lidar , 2011 .

[4]  K. Nackaerts,et al.  A fractal dimension-based modelling approach for studying the effect of leaf distribution on LAI retrieval in forest canopies , 2006 .

[5]  Yi Lin,et al.  A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements , 2010 .

[6]  A. Gonsamo,et al.  The computation of foliage clumping index using hemispherical photography , 2009 .

[7]  Alan H. Strahler,et al.  Measuring Effective Leaf Area Index, Foliage Profile, and Stand Height in New England Forest Stands Using a Full-Waveform Ground-Based Lidar , 2011 .

[8]  Pablo J. Zarco-Tejada,et al.  Field characterization of olive (Olea europaea L.) tree crown architecture using terrestrial laser scanning data , 2011 .

[9]  C. Woodcock,et al.  Estimating forest LAI profiles and structural parameters using a ground-based laser called 'Echidna'. , 2008, Tree physiology.

[10]  Yi Lin,et al.  Stop-and-Go Mode: Sensor Manipulation as Essential as Sensor Development in Terrestrial Laser Scanning , 2013, Italian National Conference on Sensors.

[11]  G. Parker,et al.  Structure and microclimate of forest canopies. , 1995 .

[12]  Richard A. Fournier,et al.  The structural and radiative consistency of three-dimensional tree reconstructions from terrestrial lidar , 2009 .

[13]  K. Omasa,et al.  Factors contributing to accuracy in the estimation of the woody canopy leaf area density profile using 3D portable lidar imaging. , 2007, Journal of experimental botany.

[14]  Yi Lin,et al.  Mini-UAV-Borne LIDAR for Fine-Scale Mapping , 2011, IEEE Geoscience and Remote Sensing Letters.

[15]  Stefan Fleck,et al.  Analyzing forest canopies with ground-based laser scanning: A comparison with hemispherical photography , 2012 .

[16]  A. Gonsamo,et al.  A new look at top-of-canopy gap fraction measurements from high-resolution airborne imagery. , 2009 .

[17]  N. Coops,et al.  Comparing canopy metrics derived from terrestrial and airborne laser scanning in a Douglas-fir dominated forest stand , 2010, Trees.

[18]  N. Coops,et al.  Assessment of standing wood and fiber quality using ground and airborne laser scanning: A review , 2011 .

[19]  Alemu Gonsamo,et al.  Sampling gap fraction and size for estimating leaf area and clumping indices from hemispherical photographs , 2010 .

[20]  Pol Coppin,et al.  3D modeling of light interception in heterogeneous forest canopies using ground-based LiDAR data , 2011, International Journal of Applied Earth Observation and Geoinformation.

[21]  Sylvain G. Leblanc,et al.  Methodology comparison for canopy structure parameters extraction from digital hemispherical photography in boreal forests , 2005 .

[22]  Benjamin Koetz,et al.  Forest Canopy Gap Fraction From Terrestrial Laser Scanning , 2007, IEEE Geoscience and Remote Sensing Letters.

[23]  Guang Zheng,et al.  Retrieving Forest Inventory Variables with Terrestrial Laser Scanning (TLS) in Urban Heterogeneous Forest , 2011, Remote. Sens..

[24]  Matti Maltamo,et al.  Airborne discrete-return LIDAR data in the estimation of vertical canopy cover, angular canopy closure and leaf area index , 2011 .

[25]  Martin G. Barker,et al.  Forest canopy research: sampling problems, and some solutions , 2001 .

[26]  Arko Lucieer,et al.  Development of a UAV-LiDAR System with Application to Forest Inventory , 2012, Remote. Sens..

[27]  M. Fournier,et al.  The use of terrestrial LiDAR technology in forest science: application fields, benefits and challenges , 2011, Annals of Forest Science.

[28]  N. Coops,et al.  A simple technique for co-registration of terrestrial LiDAR observations for forestry applications , 2012 .

[29]  Pol Coppin,et al.  Assessment of automatic gap fraction estimation of forests from digital hemispherical photography , 2005 .

[30]  Kasper Johansen,et al.  Evaluation of terrestrial laser scanners for measuring vegetation structure , 2012 .

[31]  Christopher Potter,et al.  Simulation modeling of nitrogen trace gas emissions along an age gradient of tropical forest soils , 1997 .

[32]  N. Breda Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. , 2003, Journal of experimental botany.

[33]  T. W. Ridler,et al.  Picture thresholding using an iterative selection method. , 1978 .

[34]  Lewis Graham,et al.  Mobile Mapping Systems Overview , 2010 .

[35]  J. Welles,et al.  Canopy structure measurement by gap fraction analysis using commercial instrumentation , 1996 .

[36]  J. Ross The radiation regime and architecture of plant stands , 1981, Tasks for vegetation sciences 3.

[37]  F. Fassi,et al.  Surveying and modelling the main spire of Milan Cathedral using multiple data sources , 2011 .

[38]  Nicholas C. Coops,et al.  Bias in lidar-based canopy gap fraction estimates , 2013 .

[39]  I. Jonckheere,et al.  Influence of measurement set-up of ground-based LiDAR for derivation of tree structure , 2006 .

[40]  Pol Coppin,et al.  The Properties of Terrestrial Laser System Intensity for Measuring Leaf Geometries: A Case Study with Conference Pear Trees (Pyrus Communis) , 2011, Sensors.

[41]  Pol Coppin,et al.  Sampling design of ground-based lidar measurements of forest canopy structure and its effect on shadowing , 2008 .

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