Individual Tree Species Classification by Illuminated - Shaded Area Separation

A new method, called Illumination Dependent Colour Channels (IDCC), is presented to improve individual tree species classification. The method is based on tree crown division into illuminated and shaded parts on a digital aerial image. Colour values of both sides of the tree crown are then used in species classification. Tree crown division is achieved by comparing the projected location of an aerial image pixel with its neighbours on a Canopy Height Model (CHM), which is calculated from a synchronized LIDAR point cloud. The sun position together with the mapping aircraft position are also utilised in illumination status detection. The new method was tested on a dataset of 295 trees and the classification results were compared with ones measured with two other feature extraction methods. The results of the developed method gave a clear improvement in overall tree species classification accuracy.

[1]  F. Gougeon A Crown-Following Approach to the Automatic Delineation of Individual Tree Crowns in High Spatial Resolution Aerial Images , 1995 .

[2]  P. Meyera,et al.  Semi-automated procedures for tree species identification in high spatial resolution data from digitized colour infrared-aerial photography , 1996 .

[3]  M. Rudemo,et al.  Stem number estimation by kernel smoothing of aerial photos , 1996 .

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

[5]  Morten Larsen,et al.  Optimizing templates for finding trees in aerial photographs , 1998, Pattern Recognit. Lett..

[6]  Sylvain G. Leblanc,et al.  Multiple-scattering scheme useful for geometric optical modeling , 2001, IEEE Trans. Geosci. Remote. Sens..

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

[8]  J. Franklin,et al.  Rationale and conceptual framework for classification approaches to assess forest resources and properties , 2003 .

[9]  I. Reda,et al.  Solar position algorithm for solar radiation applications , 2004 .

[10]  J. Holmgren,et al.  TREE SPECIES CLASSIFICATION OF INDIVIDUAL TREES IN SWEDEN BY COMBINING HIGH RESOLUTION LASER DATA WITH HIGH RESOLUTION NEAR-INFRARED DIGITAL IMAGES , 2004 .

[11]  I. Korpela Individual tree measurements by means of digital aerial photogrammetry , 2004, Silva Fennica Monographs.

[12]  P. Litkey,et al.  Algorithms and methods of airborne laser-scanning for forest measurements , 2004 .

[13]  D. King,et al.  Development and evaluation of an automated tree detection-delineation algorithm for monitoring regenerating coniferous forests , 2005 .

[14]  Kenneth Olofsson,et al.  Comparison of three individual tree crown detection methods , 2005, Machine Vision and Applications.

[15]  J. Hyyppä,et al.  DETECTING AND ESTIMATING ATTRIBUTES FOR SINGLE TREES USING LASER SCANNER , 2006 .

[16]  B. Koch,et al.  Detection of individual tree crowns in airborne lidar data , 2006 .

[17]  M. Rautiainen,et al.  Backscattering measurements from individual Scots pine needles. , 2007, Applied optics.

[18]  N. Coops,et al.  Multi-Angle Remote Sensing of Forest Light Use Efficiency , 2007 .

[19]  D. Donoghue,et al.  Remote sensing of species mixtures in conifer plantations using LiDAR height and intensity data , 2007 .

[20]  Xinlian Liang,et al.  Waveform features for tree identification , 2007 .

[21]  Morten Larsen Single tree species classification with a hypothetical multi-spectral satellite , 2007 .

[22]  E. Bork,et al.  Integrating LIDAR data and multispectral imagery for enhanced classification of rangeland vegetation: A meta analysis , 2007 .

[23]  J. Hyyppä,et al.  EXPERIENCES AND POSSIBILITIES OF ALS BASED FOREST INVENTORY IN FINLAND , 2007 .

[24]  K. Bang,et al.  COMPARATIVE ANALYSIS OF ALTERNATIVE METHODOLOGIES FOR TRUE ORTHO-PHOTO GENERATION FROM HIGH RESOLUTION SATELLITE IMAGERY , 2007 .

[25]  I. Korpela,et al.  Appraisal of seedling stand vegetation with airborne imagery and discrete-return LiDAR : an exploratory analysis , 2008 .

[26]  B. Koch,et al.  Full automatic detection of tree species based on delineated single tree crowns - a data fusion approach for airborne laser scanning data and aerial photographs. , 2008 .

[27]  C. Ginzler,et al.  Potential and limits of extraction of forest attributes by fusion of medium point density LiDAR data with ADS40 and RC30 images. , 2008 .

[28]  T. A. Black,et al.  Separating physiologically and directionally induced changes in PRI using BRDF models , 2008 .

[29]  M. Rautiainen,et al.  Seasonal reflectance trends of hemiboreal birch forests , 2009 .

[30]  P. Gessler,et al.  Characterizing forest succession with lidar data: An evaluation for the Inland Northwest, USA , 2009 .

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

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