Growing stock estimation for alpine forests in Austria: a robust lidar-based approach

The overall goal of this study was to describe a novel area-based semiempirical model for estimating growing stock from small-footprint light detection and ranging (lidar) data. The model assumes a linear relationship between growing stock and lidar-derived canopy volume that is stratified according to several canopy height classes to account for height dependent differences in canopy structure and nonlinear tree size-shape relationships. It was applied over a 128 km2 alpine area in Austria where operational forest inventory data and lidar data acquired in winter and summer were available. The analysis showed that the semiempirical model was quite robust against changes in laser point density and acquisition time. Further, it was found that the model performed as well as a widely used iterative regression method based on a multiplicative model. Both models reached a high coefficient of determination (R2 = 0.76–0.86) and a standard deviation of the residuals in the order of 20.4%–29.1%. Although it is less...

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

[2]  Martin Flood,et al.  Laser altimetry : From science to commercial lidar mapping , 2001 .

[3]  Juha Hyyppä,et al.  Effects of flight altitude on tree height estimation using airborne laser scanning , 2004 .

[4]  M. Nilsson.  Estimation of tree heights and stand volume using an airborne lidar system , 1996 .

[5]  C. Hopkinson.  The influence of flying altitude, beam divergence, and pulse repetition frequency on laser pulse return intensity and canopy frequency distribution , 2007 .

[6]  Guoqing Sun,et al.  Modeling lidar returns from forest canopies , 2000, IEEE Trans. Geosci. Remote. Sens..

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

[8]  Paul A. Longley,et al.  The Importance of Understanding Error in Lidar Digital Elevation Models , 2004 .

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

[10]  Norbert Pfeifer,et al.  APPLICATIONS OF THE ROBUST INTERPOLATION FOR DTM DETERMINATION , 2002 .

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

[12]  E. Næsset.  Airborne laser scanning as a method in operational forest inventory: Status of accuracy assessments accomplished in Scandinavia , 2007 .

[13]  Juha Hyyppä,et al.  Comparison of Area-Based and Individual Tree-Based Methods for Predicting Plot-Level Forest Attributes , 2010, Remote. Sens..

[14]  Terje Gobakken,et al.  Assessing effects of laser point density, ground sampling intensity, and field sample plot size on biophysical stand properties derived from airborne laser scanner data , 2008 .

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

[16]  Guoqing Sun,et al.  Inversion of a lidar waveform model for forest biophysical parameter estimation , 2006, IEEE Geoscience and Remote Sensing Letters.

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

[18]  Johannes Breidenbach,et al.  Estimation of diameter distributions by means of airborne laser scanner data , 2008 .

[19]  Aloysius Wehr,et al.  Airborne laser scanning—an introduction and overview , 1999 .

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

[21]  E. Næsset.  Determination of mean tree height of forest stands using airborne laser scanner data , 1997 .

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

[23]  K. Lim,et al.  Examining the effects of sampling point densities on laser canopy height and density metrics , 2008 .

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

[25]  T. Hassard,et al.  Applied Linear Regression , 2005 .

[26]  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 .

[27]  W. Wagner,et al.  Accuracy of large-scale canopy heights derived from LiDAR data under operational constraints in a complex alpine environment , 2006 .

[28]  Chris Hopkinson,et al.  The influence of flying altitude and beam divergence on canopy penetration and laser pulse return distribution characteristics , 2007 .

[29]  Terje Gobakken,et al.  Comparing regression methods in estimation of biophysical properties of forest stands from two different inventories using laser scanner data , 2005 .

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

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

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

[33]  D. Tiede,et al.  Assessing mountain forest structure using airborne laser scanning and landscape metrics , 2006 .

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

[35]  D. Roberts,et al.  Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape , 2004 .

[36]  Bruce E. Borders,et al.  Sampling techniques for forest resource inventory. , 1996 .

[37]  W. Cohen,et al.  Surface lidar remote sensing of basal area and biomass in deciduous forests of eastern Maryland, USA , 1999 .

[38]  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.

[39]  W. Cohen,et al.  Geographic variability in lidar predictions of forest stand structure in the Pacific Northwest , 2005 .

[40]  N. Pfeifer,et al.  DERIVATION OF DIGITAL TERRAIN MODELS IN THE SCOP++ ENVIRONMENT , 2001 .

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

[42]  Markus Hollaus,et al.  A new automated approach for co-registration of national forest inventory and airborne laser scanning data. , 2008 .

[43]  George Vosselman,et al.  Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds , 2004 .

[44]  S. Weisberg.  Applied Linear Regression: Weisberg/Applied Linear Regression 3e , 2005 .

[45]  W. Krabill,et al.  Gross-merchantable timber volume estimation using an airborne lidar system , 1986 .

[46]  W. Wagner,et al.  An application-oriented automated approach for co-registration of forest inventory and airborne laser scanning data , 2010 .

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