Direct and indirect site index determination for Norway spruce and Scots pine using bitemporal airborne laser scanner data

Abstract Forest site productivity, usually represented by site index (SI), is a fundamental resource variable in forest management planning as it is a quantitative measure of the production capacity of forest land. Site index is usually derived from estimates of dominant height (Hdom) at a given reference age using empirical age-height curves. However, it is commonly quantified with large uncertainty in forest management inventories, resulting in economic losses due to incorrect management decisions. In this study, we used bitemporal airborne laser scanner (ALS) data acquired for a study area in southeastern Norway with a time interval of 15 years to estimate SI by means of an area-based approach. We present two practical methods for SI determination, i.e., the (1) direct and (2) indirect method. With the direct method, we regressed field observations of age-height SI against canopy height metrics derived from ALS data from the first point in time and changes in ALS metrics reflecting canopy height growth during the observation period. With the indirect method, we first modelled Hdom for the two points in time using the respective ALS metrics as predictors. We then derived SI from the initial Hdom, the estimated Hdom increment, and the length of the observation period using empirical SI curves. We used bitemporal field data collected from 80 georeferenced sample plots of size 232.9 m2 to fit the species-specific regression models for SI and Hdom. We then applied the models to an independent dataset comprising 42 georeferenced validation plots of size ∼3700 m2, for which ground reference values were collected at both points in time, to assess the precision of both methods. Both the proposed methods produced SI estimates with satisfactory precision. For the direct method, the independent validation revealed root mean squared errors (RMSE) of 1.78 and 1.08 m for Norway spruce and Scots pine, respectively, compared to 1.82 m obtained for both tree species using the indirect method. The indirect method can provide a good alternative to the direct method as field observations of SI are not required to calibrate the regression models.

[1]  Estimativa da altura dominante em povoamentos decíduos através de dados LIDAR com múltiplos retornos Estimating dominant height in deciduous stands using multi-echo LIDAR data , 2010 .

[2]  Paul D. Pickell,et al.  Estimating Forest Site Productivity Using Airborne Laser Scanning Data and Landsat Time Series , 2015 .

[3]  A. Schuck,et al.  Increasing forest growth in europe — possible causes and implications for sustainable forest management , 2002 .

[4]  Barbara Koch,et al.  Exploring full-waveform LiDAR parameters for tree species classification , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[5]  R. Monserud,et al.  Genetic and Environmental Components of Variation of Site Index in Inland Douglas-Fir , 1990, Forest Science.

[6]  Juha Hyyppä,et al.  Site type estimation using airborne laser scanning and stand register data , 2010 .

[7]  E. Næsset Effects of different sensors, flying altitudes, and pulse repetition frequencies on forest canopy metrics and biophysical stand properties derived from small-footprint airborne laser data , 2009 .

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

[9]  Jerome K. Vanclay,et al.  Forest site productivity: a review of the evolution of dendrometric concepts for even-aged stands , 2008 .

[10]  E. Næsset Accuracy of forest inventory using airborne laser scanning: evaluating the first nordic full-scale operational project , 2004 .

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

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

[13]  Michele Dalponte,et al.  Tree Species Classification in Boreal Forests With Hyperspectral Data , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Han Y. H. Chen,et al.  The Influence of Recent Climate Change on Tree Height Growth Differs with Species and Spatial Environment , 2011, PloS one.

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

[16]  H. Peltola,et al.  Dynamics of daily height growth in Scots pine trees at elevated temperature and CO2 , 2005, Trees.

[17]  Tron Eid,et al.  Use of uncertain inventory data in forestry scenario models and consequential incorrect harvest decisions. , 2000 .

[18]  Terje Gobakken,et al.  Estimating forest growth using canopy metrics derived from airborne laser scanner data , 2005 .

[19]  N. Coops Characterizing Forest Growth and Productivity Using Remotely Sensed Data , 2015, Current Forestry Reports.

[20]  G. Nigh,et al.  How well can we select undamaged site trees for estimating site index , 1999 .

[21]  P. Lejeune,et al.  Modelling the top-height growth and site index of Norway spruce in Southern Belgium , 2013 .

[22]  M. Vasilescu Standard error of tree height using Vertex III. , 2013 .

[23]  Cédric Véga,et al.  Mapping site index and age by linking a time series of canopy height models with growth curves , 2009 .

[24]  Geoff Smith,et al.  The characterisation and measurement of land cover change through remote sensing: problems in operational applications? , 2003 .

[25]  J. Hyyppä,et al.  Change Detection Techniques for Canopy Height Growth Measurements Using Airborne Laser Scanner Data , 2006 .

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

[27]  Mariusz Ciesielski,et al.  Modelling top height growth and site index using repeated laser scanning data , 2017 .

[28]  M. Maltamo,et al.  ALS-based estimation of plot volume and site index in a eucalyptus plantation with a nonlinear mixed-effect model that accounts for the clone effect , 2011, Annals of Forest Science.

[29]  H. M. Steven,et al.  Forest Management , 2020, Nature.

[30]  Ronald J. Hall,et al.  The uncertainty in conifer plantation growth prediction from multi-temporal lidar datasets , 2008 .

[31]  H. Burkhart,et al.  Top height definition and its effect on site index determination in thinned and unthinned loblolly pine plantations , 2002 .

[32]  G. Namkoong,et al.  Genetic correlations for growth rhythm and growth capacity at ages 3–8 years in provenance hybrids of Picea abies , 1994 .

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

[34]  Yang Chen,et al.  Site quality assessment of a Pinus radiata plantation in Victoria, Australia, using LiDAR technology , 2012 .

[35]  A. Schönau 7. Problems in Using Vegetation or Soil Classification in Determining Site Quality , 1987 .

[36]  E. Næsset,et al.  Effects of terrain slope and aspect on the error of ALS-based predictions of forest attributes , 2018 .

[37]  C. W. Ralston,et al.  Evaluation of Forest Site Productivity , 1964 .

[38]  S. Running,et al.  Impacts of climate change on natural forest productivity – evidence since the middle of the 20th century , 2006 .

[39]  T. Eid,et al.  Modelling dominant height growth from national forest inventory individual tree data with short time series and large age errors , 2011 .

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

[41]  Joanne C. White,et al.  A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach , 2013 .

[42]  J. Hyyppä,et al.  Automatic detection of harvested trees and determination of forest growth using airborne laser scanning , 2004 .

[43]  Richard A. Birdsey,et al.  Airborne laser scanner-assisted estimation of aboveground biomass change in a temperate oak–pine forest , 2014 .

[44]  Robert A. Monserud,et al.  Height Growth and Site Index Curves for Inland Douglas-fir Based on Stem Analysis Data and Forest Habitat Type , 1984 .

[45]  U. Diéguez-Aranda,et al.  Evaluation of age-independent methods of estimating site index and predicting height growth: a case study for maritime pine in Asturias (NW Spain) , 2014, European Journal of Forest Research.

[46]  Sanford Weisberg,et al.  An R Companion to Applied Regression , 2010 .

[47]  E. Næsset,et al.  Forestry applications of airborne laser scanning : concepts and case studies , 2014 .

[48]  L. Bruzzone,et al.  Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data , 2012 .

[49]  R. A. Studhalter Tree growth , 2008, The Botanical Review.

[50]  B. Turner,et al.  Growth and yield modelling of Australian eucalypt forests II. Future trends , 1990 .

[51]  Erik Næsset,et al.  Effects of different sensors and leaf-on and leaf-off canopy conditions on echo distributions and individual tree properties derived from airborne laser scanning , 2010 .

[52]  Erik Næsset,et al.  Detection of biomass change in a Norwegian mountain forest area using small footprint airborne laser scanner data , 2013, Stat. Methods Appl..

[53]  J. Bontemps,et al.  Predictive approaches to forest site productivity: recent trends, challenges and future perspectives , 2014 .

[54]  Michele Dalponte,et al.  Individual tree crown approach for predicting site index in boreal forests using airborne laser scanning and hyperspectral data , 2017, Int. J. Appl. Earth Obs. Geoinformation.