Measures of spatial forest structure derived from airborne laser data are associated with natural regeneration patterns in an uneven-aged spruce forest

The relationships between measures of forest structure as derived from airborne laser scanner data and the variation in quantity (Q) and vitality (V) of young trees in a size-diverse spruce forest were analyzed. A regeneration success rate (Q), leader length (V), relative leader length (V), and apical dominance ratio (V) were regressed against 27 different laser-derived explanatory variables representing three different spatial scales. The resulting 81 different models for each response variable were ranked according to their Akaike information criterion score and significance level. Each laser variable was then associated with four categories. These were scale, return, fraction, and type. Within the scale category, laser variables were grouped according to the spatial scale from which they originated. Similarly, within the return, fraction, and type categories, the variables were grouped according to if they originated from first or last return echoes; if they originated from lower, middle, or upper fraction of the range of laser heights or values derived from the full range of laser pulses, and if they were canopy height or canopy density metrics. The results show that the laser variables were strongest correlated with the quantity of small trees and that these variables could be attributed to large-scale, last return, lower fraction, and density metrics. The correlations with the vitality responses were weaker, but the results indicate that variables derived from a smaller scale than for the quantity were better in order to explain variation in leader length, relative leader length, and apical dominance ratio.

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

[2]  B. Økland Mycetophilidae (Diptera), an insect group vulnerable to forestry practices? A comparison of clearcut, managed and semi-natural spruce forests in southern Norway , 1994, Biodiversity & Conservation.

[3]  W. W. Carson,et al.  Accuracy of a high-resolution lidar terrain model under a conifer forest canopy , 2003 .

[4]  Alex C. Lee,et al.  Quantifying Australian forest floristics and structure using small footprint LiDAR and large scale aerial photography , 2006 .

[5]  T. Kuuluvainen,et al.  Seedling establishment in relation to microhabitat variation in a windthrow gap in a boreal Pinus sylvestris forest , 1998 .

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

[7]  M. Hodgson,et al.  Accuracy of Airborne Lidar-Derived Elevation: Empirical Assessment and Error Budget , 2004 .

[8]  T. Kuuluvainen Gap disturbance, ground microtopography, and the regeneration dynamics of boreal coniferous forests in Filand: a review , 1994 .

[9]  H. Hasenauer,et al.  Predicting juvenile tree height growth in uneven-aged mixed species stands in Austria , 1997 .

[10]  K. Hanssen,et al.  Performance of Sown and Naturally Regenerated Picea abies Seedlings Under Different Scarification and Harvesting Regimens , 2003 .

[11]  Geoffrey G. Parker,et al.  The canopy surface and stand development: assessing forest canopy structure and complexity with near-surface altimetry , 2004 .

[12]  J. Vose,et al.  Evaluation of the Competitive Environment for White Pine (Pinus strobus L.) Seedlings Planted on Prescribed Burn Sites in the Southern Appalachians , 1995 .

[13]  Raffaello Giannini,et al.  Influence of Light and Competition on Crown and Shoot Morphological Parameters of Norway Spruce and Silver Fir Saplings , 2005 .

[14]  C. Messier,et al.  Effects of light and intraspecific competition on growth and crown morphology of two size classes of understory balsam fir saplings , 2001 .

[15]  R. M. Newnham The development of a stand model for Douglas fir , 1964 .

[16]  Erik Næsset,et al.  Mapping defoliation during a severe insect attack on Scots pine using airborne laser scanning , 2006 .

[17]  Erik Næsset,et al.  Effects of different flying altitudes on biophysical stand properties estimated from canopy height and density measured with a small-footprint airborne scanning laser , 2004 .

[18]  Alan R. Ek,et al.  Optimizing the Management of Uneven-aged Forest Stands , 1974 .

[19]  Hans Pretzsch,et al.  The single tree-based stand simulator SILVA: construction, application and evaluation , 2002 .

[20]  H. Akaike A new look at the statistical model identification , 1974 .

[21]  E. Næsset Effects of Differential Single- and Dual-Frequency GPS and GLONASS Observations on Point Accuracy under Forest Canopies , 2001 .

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

[23]  Juha Hyyppä,et al.  Identifying and quantifying structural characteristics of heterogeneous boreal forests using laser scanner data , 2005 .

[24]  J. Vanclay Modelling regeneration and recruitment in a tropical rain forest , 1992 .

[25]  Jerome K. Vanclay,et al.  A growth model for north Queensland rainforests , 1989 .

[26]  Erik Næsset,et al.  Estimating percentile-based diameter distributions in uneven-sized Norway spruce stands using airborne laser scanner data , 2007 .

[27]  M. Karlsson,et al.  Natural regeneration of Norway spruce, Scots pine and birch under Norway spruce shelterwoods of varying densities on a mesic-dry site in southern Sweden , 2002 .

[28]  R. Monserud,et al.  Modeling individual tree mortality for Austrian forest species , 1999 .

[29]  J. Hyyppä,et al.  Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions , 2004 .

[30]  C. Messier,et al.  Morphological indicators of growth response of coniferous advance regeneration to overstorey removal in the boreal forest , 2000 .

[31]  T. Kuuluvainen,et al.  Regeneration microsites of Picea abies seedlings in a windthrow area of a boreal old-growth forest in southern Finland , 2003 .

[32]  N. Lexerød Recruitment models for different tree species in Norway , 2005 .

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

[34]  Tron Eid,et al.  Models for individual tree mortality in Norway , 2001 .