The suitability of leaf-off airborne laser scanning data in an area-based forest inventory of coniferous and deciduous trees

This study examined the suitability of airborne laser scanner (ALS) data collected under leaf-off conditions in a forest inventory, in which deciduous and coniferous trees need to be separated. All analyses were carried out with leaf-on and leaf-off ALS data collected from the same study area. Additionally, aerial photographs were utilized in the Nearest Neighbor (NN) imputations. An area-based approach was used in this study. Regression estimates of plot volume were more accurate in the case of leaf-off than leaf-on data. In addition, regression models were more accurate in coniferous plots than in deciduous plots. The results of applying leaf-on models with leaf-off data, and vice versa, indicate that leaf-on and leaf-off data should not be combined since this causes serious bias. The total volume and volume by coniferous and deciduous trees was estimated by the NN imputation. In terms of total volume, leaf-off data provided more accurate estimates than leaf-on data. In addition, leaf-off data discriminated between coniferous and deciduous trees, even without the use of aerial photographs. Accurate results were also obtained when leaf-off ALS data were used to classify sample plots into deciduous and coniferous dominated plots. The results indicate that the area-based method and ALS data collected under leaf-off conditions are suitable for forest inventory in which deciduous and coniferous trees need to be distinguished.

[1]  M. Maltamo,et al.  The k-MSN method for the prediction of species-specific stand attributes using airborne laser scanning and aerial photographs , 2007 .

[2]  Juha Hyyppä,et al.  DECIDUOUS-CONIFEROUS TREE CLASSIFICATION USING DIFFERENCE BETWEEN FIRST AND LAST PULSE LASER SIGNATURES , 2007 .

[3]  Christina Gloeckner,et al.  Modern Applied Statistics With S , 2003 .

[4]  Tomas Brandtberg.  Classifying individual tree species under leaf-off and leaf-on conditions using airborne lidar , 2007 .

[5]  Ilkka Korpela,et al.  Mapping of understory lichens with airborne discrete-return LiDAR data , 2008 .

[6]  Jouko Laasasenaho.  Taper curve and volume functions for pine, spruce and birch [Pinus sylvestris, Picea abies, Betula pendula, Betula pubescens] , 1982 .

[7]  M. Hodgson,et al.  An evaluation of LIDAR- and IFSAR-derived digital elevation models in leaf-on conditions with USGS Level 1 and Level 2 DEMs , 2003 .

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

[9]  Markus Hollaus,et al.  Tree species classification based on full-waveform airborne laser scanning data , 2009 .

[10]  Tomas Brandtberg.  Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density lidar data from the eastern deciduous forest in North America , 2003 .

[11]  M. Maltamo,et al.  A Two Stage Method to Estimate Species-specific Growing Stock , 2009 .

[12]  M. Maltamo,et al.  Combining ALS and NFI training data for forest management planning: a case study in Kuortane, Western Finland , 2009, European Journal of Forest Research.

[13]  H. Andersen,et al.  Tree species differentiation using intensity data derived from leaf-on and leaf-off airborne laser scanner data , 2009 .

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

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

[16]  Manfred Näslund,et al.  Skogsförsöksanstaltens gallringsförsök i tallskog , 1936 .

[17]  Åsa Persson,et al.  Detecting and measuring individual trees using an airborne laser scanner , 2002 .

[18]  James J. Little,et al.  Automatic extraction of Irregular Network digital terrain models , 1979, SIGGRAPH.

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

[20]  E. Næsset,et al.  Classifying species of individual trees by intensity and structure features derived from airborne laser scanner data , 2009 .

[21]  Richard K. Broughton,et al.  Mapping the understorey of deciduous woodland from leaf-on and leaf-off airborne LiDAR data: A case study in lowland Britain , 2009 .

[22]  E. Næsset,et al.  Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve , 2002 .

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

[24]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[25]  Annika Kangas,et al.  Estimating Individual Tree Growth with the k-Nearest Neighbour and k-Most Similar Neighbour Methods , 2001 .

[26]  J. Reitberger,et al.  Analysis of full waveform LIDAR data for the classification of deciduous and coniferous trees , 2008 .

[27]  U. Garczarek.  Classification rules in standardized partition spaces , 2002 .

[28]  ON THE QUALITY CHECKING OF THE AIRBORNE LASER SCANNING-BASED NATION WIDE ELEVATION MODEL IN FINLAND , 2008 .

[29]  Albert R. Stage,et al.  Most Similar Neighbor: An Improved Sampling Inference Procedure for Natural Resource Planning , 1995, Forest Science.

[30]  J. Holmgren.  Prediction of tree height, basal area and stem volume in forest stands using airborne laser scanning , 2004 .