Tree species classification based on stem-related feature parameters derived from static terrestrial laser scanning data

ABSTRACT Tree species information is crucial for forest ecology and management, and development of techniques efficient for tree species classification has long been highlighted. In order to fulfil this task, a large variety of remote-sensing technologies have been attempted. Static terrestrial laser scanning (TLS) is such a representative case, which has proved to be capable of deriving explicit tree structure feature parameters (ETSPs) and has been primarily validated for tree species classification. However, in practice for each forest plot mapped by TLS, this kind of ETSP-based solutions can only work for the first circle layer of individual trees surrounding the TLS systems, because the trees at the outer circle layers tend to show incomplete crown representations due to the effect of laser obscuration. This adverse circumstance even may occur to the scenario of TLS-based inventory in the multi-scan mode. To break through this restriction, this study focused on tree stems that tend to be more readily mapped by TLS in the complicated forest environment, and then, their comparatively complete forms were used to comprehensively derive primarily stem-related feature parameters (SRPs) for distinguishing different tree species. Specifically, in this study 14 SRPs were proposed, mainly based on stem structure and surface texture characteristics. Based on a Support Vector Machine (SVM) classifier, the classification was operated in the leave-one-out cross-validation (LOOCV) mode. In the case of four typical boreal tree species, that is, Picea abies, Pinus sylvestris, Populus tremula, and Quercus robur, tests showed that the optimal total classification accuracy reached 71.93%. Given that tree stems generally display less features than crowns, the result is acceptable. Overall, the positive results have validated the strategy of fulfilling TLS-based tree species classification by deriving predominantly stem-related feature parameters, and this, in a broad sense, can expand the effective range of TLS on forest ecological studies.

[1]  Liviu Theodor Ene,et al.  Estimating Single-Tree Crown Biomass of Norway Spruce by Airborne Laser Scanning: A Comparison of Methods with and without the Use of Terrestrial Laser Scanning to Obtain the Ground Reference Data , 2014 .

[2]  David L.B. Jupp,et al.  Measuring tree stem diameters using intensity profiles from ground-based scanning lidar from a fixed viewpoint , 2011 .

[3]  Martin Herold,et al.  Implications of sensor configuration and topography on vertical plant profiles derived from terrestrial LiDAR , 2014 .

[4]  Barbara Koch,et al.  Investigating multiple data sources for tree species classification in temperate forest and use for single tree delineation , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[5]  Richard A. Fournier,et al.  Predicting wood fiber attributes using local-scale metrics from terrestrial LiDAR data: A case study of Newfoundland conifer species , 2015 .

[6]  Yi Lin,et al.  Tree Height Growth Measurement with Single-Scan Airborne, Static Terrestrial and Mobile Laser Scanning , 2012, Sensors.

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

[8]  Gregory Asner,et al.  Mapping Savanna Tree Species at Ecosystem Scales Using Support Vector Machine Classification and BRDF Correction on Airborne Hyperspectral and LiDAR Data , 2012, Remote. Sens..

[9]  Markus Holopainen,et al.  Effect of data acquisition accuracy on timing of stand harvests and expected net present value. , 2006 .

[10]  Paolo Remagnino,et al.  Plant species identification using digital morphometrics: A review , 2012, Expert Syst. Appl..

[11]  Norbert Pfeifer,et al.  Delineation of Tree Crowns and Tree Species Classification From Full-Waveform Airborne Laser Scanning Data Using 3-D Ellipsoidal Clustering , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[12]  R. Hall,et al.  Incorporating texture into classification of forest species composition from airborne multispectral images , 2000 .

[13]  Kenji Omasa,et al.  Voxel-Based 3-D Modeling of Individual Trees for Estimating Leaf Area Density Using High-Resolution Portable Scanning Lidar , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Alan H. Strahler,et al.  Retrieval of forest structural parameters using a ground-based lidar instrument (Echidna®) , 2008 .

[16]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[17]  Lew Fock Chong Lew Yan Voon,et al.  Single tree species classification from Terrestrial Laser Scanning data for forest inventory , 2013, Pattern Recognit. Lett..

[18]  Juha Hyyppä,et al.  Detection of Vertical Pole-Like Objects in a Road Environment Using Vehicle-Based Laser Scanning Data , 2010, Remote. Sens..

[19]  C. Hopkinson,et al.  Assessing forest metrics with a ground-based scanning lidar , 2004 .

[20]  M. Cho,et al.  Classification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and LiDAR data in a Random Forest data mining environment , 2012 .

[21]  N. Pfeifer,et al.  Three-dimensional reconstruction of stems for assessment of taper, sweep and lean based on laser scanning of standing trees , 2004 .

[22]  D. Hoekman,et al.  Review of relationships between grey-tone co-occurrence, semivariance, and autocorrelation based image texture analysis approaches , 2005 .

[23]  Juha Hyyppä,et al.  Tree mapping using airborne, terrestrial and mobile laser scanning – A case study in a heterogeneous urban forest , 2013 .

[24]  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 .

[25]  Philip Lewis,et al.  Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data , 2013, Remote. Sens..

[26]  Hitendra Padalia,et al.  Forest tree species discrimination in western Himalaya using EO-1 Hyperion , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[27]  Diego González-Aguilera,et al.  An automatic procedure for co-registration of terrestrial laser scanners and digital cameras , 2009 .

[28]  Charles O. Sabatia,et al.  Predicting site index of plantation loblolly pine from biophysical variables , 2014 .

[29]  Alan H. Strahler,et al.  Finding Leaves in the Forest: The Dual-Wavelength Echidna Lidar , 2015, IEEE Geoscience and Remote Sensing Letters.

[30]  F. Putz,et al.  Ecological characterization of tree species for guiding forest management decisions in seasonally dry forests in Lomerı́o, Bolivia , 1999 .

[31]  Johan Holmgren,et al.  Tree Stem and Height Measurements using Terrestrial Laser Scanning and the RANSAC Algorithm , 2014, Remote. Sens..

[32]  Baoxin Hu,et al.  Individual Urban Tree Species Classification Using Very High Spatial Resolution Airborne Multi-Spectral Imagery Using Longitudinal Profiles , 2012, Remote. Sens..

[33]  S. Popescu,et al.  Classification of Tree Species in Overstorey Canopy of Subtropical Forest Using QuickBird Images , 2015, PloS one.

[34]  A. Antonarakis,et al.  Evaluating forest biometrics obtained from ground lidar in complex riparian forests , 2011 .

[35]  Åsa Persson,et al.  Identifying species of individual trees using airborne laser scanner , 2004 .

[36]  Juha Hyyppä,et al.  Automatic Stem Mapping Using Single-Scan Terrestrial Laser Scanning , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[37]  C. Leuschner,et al.  Crown plasticity in mixed forests—Quantifying asymmetry as a measure of competition using terrestrial laser scanning , 2011 .

[38]  P. Radtke,et al.  Detailed Stem Measurements of Standing Trees from Ground-Based Scanning Lidar , 2006, Forest Science.

[39]  Juha Hyyppä,et al.  Automatic Stem Mapping by Merging Several Terrestrial Laser Scans at the Feature and Decision Levels , 2013, Sensors.

[40]  P. Litkey,et al.  Tree species classification from fused active hyperspectral reflectance and LIDAR measurements. , 2010 .

[41]  Yi Lin,et al.  Tree species classification based on explicit tree structure feature parameters derived from static terrestrial laser scanning data , 2016 .

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

[43]  Juha Hyyppä,et al.  Assessment of Low Density Full-Waveform Airborne Laser Scanning for Individual Tree Detection and Tree Species Classification , 2014 .