ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-3-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 697–704, 2022
https://doi.org/10.5194/isprs-annals-V-3-2022-697-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 697–704, 2022
https://doi.org/10.5194/isprs-annals-V-3-2022-697-2022
 
17 May 2022
17 May 2022

A VOXEL-BASED METHOD FOR THE THREE-DIMENSIONAL MODELLING OF HEATHLAND FROM LIDAR POINT CLOUDS: FIRST RESULTS

N. Homainejad1, S. Zlatanova1, and N. Pfeifer2 N. Homainejad et al.
  • 1University of New South Wales, Faculty of Built Environment, High St Kensington, NSW, Australia
  • 2Department of Geodesy and Geoinformation, Technische Universität Wien, Vienna, Austria

Keywords: Bushfire, Vegetation, Canopy Layers, Voxel, 3D Model, Segmentation, Classification

Abstract. Bushfires are an intrinsic part of the New South Wales’ (NSW) environment in Australia, especially in the Blue Mountains region (11400km2), that is dominated by fire prone vegetation that includes heathland. Many of the Australian native plants in this region are fire-prone and combustible, and many species even require fire to regenerate. The classification of the lateral and vertical distribution of living vegetation is necessary to manage the complexity of bushfires. Currently, interpretation of aerial and satellite images is the prevalent method for the classification of vegetation in NSW. The result does not represent important vegetation structural attributes, such as vegetation height, subcanopy height, and destiny. This paper presents an automated method for the three-dimensional modelling of heathland and important heathland parameters, such as heath shrub height and continuity, and sparse tree and mallee height and density in support of bushfire behaviour modelling. For this study airborne lidar point clouds with a density of 120 points per square meter are used. For the processing and modelling the study is divided into a point cloud processing phase and a voxel-based modelling phase. The point cloud processing phase consists of the normalisation of the height and extraction of the above ground vegetation, while the voxel phase consists of seeded region growing for segmentation, and K-means clustering for the classification of the vegetation into three different canopy layers: a) heath shrubs, b) sparse trees and mallee, c) tall trees.