A hybrid method for segmenting individual trees from airborne lidar data

Abstract Segmentation of individual trees from airborne lidar data uses either the point cloud directly or canopy height models (CHMs) derived from the point cloud. Point-based segmentation is able to detect understorey trees but is sensitive to the point density and often demands a high overhead cost of computing. Conversely, CHM-based segmentation can be easily implemented but it is impractical for the detection of understorey trees. To identify highly accurate treetops as well as understorey trees, this paper presents a hybrid method by modifying a CHM-based individual tree crown delineation (ITCD) algorithm and integrating it into a point-based algorithm. A multiscale local maxima (LM) algorithm is developed to improve the accuracy of LM obtained from CHMs in different spatial resolutions. The improved LM are used as seeds to segment the lidar point cloud into individual trees. For each tree, histogram analysis is applied to investigate the presence of understorey trees. Field measurements of tree heights and crown widths are used as ground truth to evaluate how well the proposed method is performing. The mean errors of tree heights and crown widths are 0.147 m and −0.004 m, respectively. The proposed method is also compared with five conventional methods of individual tree segmentation, namely ITCD, fixed window local maxima, Popescu and Wynne’s local maxima, variable area local maxima, and Li’s point-based segmentation. The comparison results indicate that the proposed hybrid method outperforms the conventional methods in terms of detection rate, omission error, commission error, mean absolute error of tree heights and root-mean-squared-error of tree heights.

[1]  Randolph H. Wynne,et al.  Estimating plot-level tree heights with lidar : local filtering with a canopy-height based variable window size , 2002 .

[2]  Tomáš Bucha,et al.  Integration of tree allometry rules to treetops detection and tree crowns delineation using airborne lidar data , 2017 .

[3]  Paul E. Gessler,et al.  The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using lidar data , 2008 .

[4]  Michele Dalponte,et al.  Area-based vs tree-centric approaches to mapping forest carbon in Southeast Asian forests from airborne laser scanning data , 2017 .

[5]  R. Lucas,et al.  The delineation of tree crowns in Australian mixed species forests using hyperspectral Compact Airborne Spectrographic Imager (CASI) data , 2006 .

[6]  Lorenzo Bruzzone,et al.  A Hierarchical Approach to Three-Dimensional Segmentation of LiDAR Data at Single-Tree Level in a Multilayered Forest , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[7]  P. Gong,et al.  Isolating individual trees in a savanna woodland using small footprint lidar data , 2006 .

[8]  Tomislav Hengl,et al.  Finding the right pixel size , 2006, Comput. Geosci..

[9]  J. Hyyppä,et al.  Review of methods of small‐footprint airborne laser scanning for extracting forest inventory data in boreal forests , 2008 .

[10]  S. Popescu,et al.  A voxel-based lidar method for estimating crown base height for deciduous and pine trees , 2008 .

[11]  Laura Chasmer,et al.  Investigating laser pulse penetration through a conifer canopy by integrating airborne and terrestrial lidar , 2006 .

[12]  Sylvie Durrieu,et al.  PTrees: A point-based approach to forest tree extraction from lidar data , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[13]  Le Wang,et al.  How to assess the accuracy of the individual tree-based forest inventory derived from remotely sensed data: a review , 2016 .

[14]  Zhen Zhen,et al.  Trends in Automatic Individual Tree Crown Detection and Delineation - Evolution of LiDAR Data , 2016, Remote. Sens..

[15]  Francesca Bovolo,et al.  Subdominant tree detection in multi-layered forests by a local projection of airborne lidar data , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[16]  Kaiguang Zhao,et al.  HIERARCHICAL WATERSHED SEGMENTATION OF CANOPY HEIGHT MODEL FOR MULTI-SCALE FOREST INVENTORY , 2007 .

[17]  Xuan Zhu,et al.  An integrated GIS tool for automatic forest inventory estimates of Pinus radiata from LiDAR data , 2013 .

[18]  Ilkka Korpela,et al.  Mapping forest plots: an efficient method combining photogrammetry and field triangulation , 2007 .

[19]  T. Noland,et al.  Automated delineation of individual tree crowns from lidar data by multi-scale analysis and segmentation , 2012 .

[20]  Maggi Kelly,et al.  A New Method for Segmenting Individual Trees from the Lidar Point Cloud , 2012 .

[21]  Linhai Jing,et al.  Improving the efficiency and accuracy of individual tree crown delineation from high-density LiDAR data , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[22]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[23]  Donald A. Falk,et al.  Application of Metabolic Scaling Theory to reduce error in local maxima tree segmentation from aerial LiDAR , 2014 .

[24]  Juha Hyyppä,et al.  USING INDIVIDUAL TREE CROWN APPROACH FOR FOREST VOLUME EXTRACTION WITH AERIAL IMAGES AND LASER POINT CLOUDS , 2005 .

[25]  S. Popescu,et al.  Seeing the Trees in the Forest: Using Lidar and Multispectral Data Fusion with Local Filtering and Variable Window Size for Estimating Tree Height , 2004 .

[26]  Helene C. Muller-Landau,et al.  Measuring tree height: a quantitative comparison of two common field methods in a moist tropical forest , 2013 .

[27]  Hamid Hamraz,et al.  Vertical stratification of forest canopy for segmentation of understory trees within small-footprint airborne LiDAR point clouds , 2016, 1701.00169.

[28]  Jun Zhang,et al.  A robust approach for tree segmentation in deciduous forests using small-footprint airborne LiDAR data , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[29]  Li Liu,et al.  Assessment of Tree Attributes Extraction Algorithms , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.