Angle count sampling reliability as ground truth for area-based LiDAR applications in forest inventories

LiDAR-based techniques to estimate forest variables at the stand level require accurate calibration through ground truth data. One purpose of this study was to verify whether angle count samples can be used as suitable ground truth to calibrate LiDAR-based models for timber volume estimation. Volume data were acquired on the ground for 79 plots in the Latemar forest (province of Bolzano, Italian Alps). A simple linear regression model, using the sum of all of the tree canopy heights in the plot as the explanatory variable, was adopted. As angle count samples have no fixed area, three different methods to approximate their size were compared. The angle count sample area can be properly approximated by visual assessment of the tree size classes and by callipering the largest tree in the plot. The results show that angle count sampling can be an efficient solution to calibrate LiDAR-based models: they produced fair estimates at the plot level (relative root mean square error (RMSE), 26.6%) that were better t...

[1]  Mark D. Semon,et al.  POSTUSE REVIEW: An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements , 1982 .

[2]  Joanne C. White,et al.  Lidar sampling for large-area forest characterization: A review , 2012 .

[3]  Frédéric Bretar,et al.  3-D mapping of a multi-layered Mediterranean forest using ALS data , 2012 .

[4]  Piermaria Corona,et al.  Area-based lidar-assisted estimation of forest standing volume , 2008 .

[5]  Walter Bitterlich,et al.  The relascope idea. Relative measurements in forestry. , 1984 .

[6]  P. Gessler,et al.  Investigating the influence of LiDAR ground surface errors on the utility of derived forest inventories , 2012 .

[7]  E. Næsset,et al.  Assessing effects of positioning errors and sample plot size on biophysical stand properties derived from airborne laser scanner data. , 2009 .

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

[9]  Jouni Siipilehto,et al.  Improving the Accuracy of Predicted Basal-Area Diameter Distribution in Advanced Stands by Determining Stem Number , 1999 .

[10]  G. Scrinzi,et al.  Stima su base LiDAR delle provvigioni legnose forestali: uno studio per la Foresta di Paneveggio , 2010 .

[11]  Patrizia Gasparini,et al.  Aboveground tree volume and phytomass prediction equations for forest species in Italy , 2011, European Journal of Forest Research.

[12]  Piermaria Corona,et al.  Airborne Laser Scanning to support forest resource management under alpine, temperate and Mediterranean environments in Italy , 2012 .

[13]  Joanne C. White,et al.  The role of LiDAR in sustainable forest management , 2008 .

[14]  G. Scrinzi,et al.  Impiego di dati lidar di pubblica disponibilità per il monitoraggio forestale a grande e piccola scala: il progetto ITALID , 2013 .

[15]  Markus Hollaus,et al.  Growing stock estimation for alpine forests in Austria: a robust lidar-based approach , 2009 .

[16]  S. Condés,et al.  Comparison of relascope and fixed-radius plots for the estimation of forest stand variables in northeast Spain: an inventory simulation approach , 2011, European Journal of Forest Research.

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

[18]  E. Tomppo,et al.  The Finnish National Forest Inventory , 1995 .

[19]  Terje Gobakken,et al.  Assessing effects of sample plot positioning errors on biophysical stand properties derived from airborne laser scanner data. , 2008 .

[20]  A. Floris,et al.  Estimating forest timber volume by means of "low-cost" LiDAR data , 2012 .

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

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

[23]  Michele Dalponte,et al.  The role of ground reference data collection in the prediction of stem volume with LiDAR data in mountain areas , 2011 .

[24]  P. Axelsson.  DEM Generation from Laser Scanner Data Using Adaptive TIN Models , 2000 .

[25]  Mark O. Kimberley,et al.  Airborne scanning LiDAR in a double sampling forest carbon inventory , 2012 .

[26]  Martin Rutzinger,et al.  Estimation of Aboveground Biomass in Alpine Forests: A Semi-Empirical Approach Considering Canopy Transparency Derived from Airborne LiDAR Data , 2010, Sensors.

[27]  Michele Dalponte,et al.  Airborne laser scanning of forest resources: An overview of research in Italy as a commentary case study , 2013, Int. J. Appl. Earth Obs. Geoinformation.

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

[29]  M. Maltamo,et al.  Testing the usability of truncated angle count sample plots as ground truth in airborne laser scanning-based forest inventories , 2007 .

[30]  R. Dubayah,et al.  Lidar Remote Sensing for Forestry , 2000, Journal of Forestry.

[31]  Lorenzo Bruzzone,et al.  Mapping and modeling forest tree volume using forest inventory and airborne laser scanning , 2011, European Journal of Forest Research.

[32]  T. Nord-Larsen,et al.  Estimation of forest resources from a country wide laser scanning survey and national forest inventory data , 2012 .

[33]  F. Grundner,et al.  Massentafeln zur Bestimmung des Holzgehaltes stehender Waldbäume und Waldbestände. , 1906 .

[34]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[35]  E. Næsset,et al.  Laser scanning of forest resources: the nordic experience , 2004 .