The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLI-B8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 585–588, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-585-2016
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 585–588, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-585-2016

  23 Jun 2016

23 Jun 2016

ACCURACY ASSESSMENT OF CROWN DELINEATION METHODS FOR THE INDIVIDUAL TREES USING LIDAR DATA

K. T Chang1, C. Lin2, Y. C. Lin3, and J. K. Liu4 K. T Chang et al.
  • 1Dept. of Civil Eng. and Environmental Informatics, Ming Hsin University of Science and Technology, Hsinchu County 30401, Taiwan
  • 2Dept. of Forestry and Natural Resources, National Chiayi University, Chiayi, Taiwan
  • 3Dept. of Environmental Information and Engineering, Chung Cheng Institute of Technology, National Defense University, Taoyuan County 30401, Taiwan
  • 4LIDAR Technology Co., Ltd., Hsinchu County 30274, Taiwan

Keywords: Forest, Canopy, LiDAR, Pit-free, Stand-level

Abstract. Forest canopy density and height are used as variables in a number of environmental applications, including the estimation of biomass, forest extent and condition, and biodiversity. The airborne Light Detection and Ranging (LiDAR) is very useful to estimate forest canopy parameters according to the generated canopy height models (CHMs). The purpose of this work is to introduce an algorithm to delineate crown parameters, e.g. tree height and crown radii based on the generated rasterized CHMs. And accuracy assessment for the extraction of volumetric parameters of a single tree is also performed via manual measurement using corresponding aerial photo pairs. A LiDAR dataset of a golf course acquired by Leica ALS70-HP is used in this study. Two algorithms, i.e. a traditional one with the subtraction of a digital elevation model (DEM) from a digital surface model (DSM), and a pit-free approach are conducted to generate the CHMs firstly. Then two algorithms, a multilevel morphological active-contour (MMAC) and a variable window filter (VWF), are implemented and used in this study for individual tree delineation. Finally, experimental results of two automatic estimation methods for individual trees can be evaluated with manually measured stand-level parameters, i.e. tree height and crown diameter. The resulting CHM generated by a simple subtraction is full of empty pixels (called "pits") that will give vital impact on subsequent analysis for individual tree delineation. The experimental results indicated that if more individual trees can be extracted, tree crown shape will became more completely in the CHM data after the pit-free process.