The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-3/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W3, 143–148, 2017
https://doi.org/10.5194/isprs-archives-XLII-3-W3-143-2017
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W3, 143–148, 2017
https://doi.org/10.5194/isprs-archives-XLII-3-W3-143-2017

  19 Oct 2017

19 Oct 2017

INDIVIDUAL TREE CROWN DELINEATION USING MULTI-WAVELENGTH TITAN LIDAR DATA

F. Naveed and B. Hu F. Naveed and B. Hu
  • Earth and Space Science and Engineering Department, Lassonde School of Engineering, York University, 4700 Keele Street, Toronto, Canada

Keywords: Multispectral LiDAR, Emerald Ash Borer, Digital Elevation Model, Digital Surface Model, Canopy Height Model, Individual Tree Crown Delineation, Seeded Region Growing, Inverse Distance Weighted Interpolation

Abstract. The inability to detect the Emerald Ash Borer (EAB) at an early stage has led to the enumerable loss of different species of ash trees. Due to the increasing risk being posed by the EAB, a robust and accurate method is needed for identifying Individual Tree Crowns (ITCs) that are at a risk of being infected or are already diseased. This paper attempts to outline an ITC delineation method that employs airborne multi-spectral Light Detection and Ranging (LiDAR) to accurately delineate tree crowns. The raw LiDAR data were initially pre-processed to generate the Digital Surface Models (DSM) and Digital Elevation Models (DEM) using an iterative progressive TIN (Triangulated Irregular Network) densification method. The DSM and DEM were consequently used for Canopy Height Model (CHM) generation, from which the structural information pertaining to the size and shape of the tree crowns was obtained. The structural information along with the spectral information was used to segment ITCs using a region growing algorithm. The availability of the multi-spectral LiDAR data allows for delineation of crowns that have otherwise homogenous structural characteristics and hence cannot be isolated from the CHM alone. This study exploits the spectral data to derive initial approximations of individual tree tops and consequently grow those regions based on the spectral constraints of the individual trees.