The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLIII-B3-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 603–608, 2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 603–608, 2021

  29 Jun 2021

29 Jun 2021


A. Novo1, H. González-Jorge2, J. Martínez-Sánchez1, J. M. Fernández-Alonso3, and H. Lorenzo4 A. Novo et al.
  • 1Geotech-Group, CINTECX, Department of Natural Resources and Environmental Engineering School of Mining Engineering, University of Vigo, 36310, Vigo, Spain
  • 2Physics Engineering Lab, School of Aerospace Engineering, University of Vigo, 32004, Ourense, Spain
  • 3Lourizán Forest Research Center Xunta de Galicia, P.O. Box 127, 36080 Pontevedra, Spain
  • 4Geotech-Group, CINTECX, Department of Natural Resources and Environmental Engineering, School of Forestry Engineering, University of Vigo, 36005 Pontevedra, Spain

Keywords: Mapping, Remote Sensing, LiDAR data, GIS analysis, Point cloud processing, Vegetation structure, Forest parameters

Abstract. Forest spatial structure describes the relationships among different species in the same forest community. Automation in the monitoring of the structural forest changes and forest mapping is one of the main utilities of applications of modern geoinformatics methods. The obtaining objective information requires the use of spatial data derived from photogrammetry and remote sensing. This paper investigates the possibility of applying light detection and ranging (LiDAR) point clouds and geographic information system (GIS) analyses for automated mapping and detection changes in vegetation structure during a year of study. The research was conducted in an area of the Ourense Province (NWSpain). The airborne laser scanning (ALS) data, acquired in August 2019 and June of 2020, reveal detailed changes in forest structure. Based on ALS data the vegetation parameters will be analysed.

To study the structural behaviour of the tree vegetation, the following parameters are used in each one of the sampling areas: (1) Relationship between the tree species present and their stratification; (2) Vegetation classification in fuel types; (3) Biomass (Gi); (4) Number of individuals per area; and (5) Canopy cover fraction (CCF). Besides, the results were compared with the ground truth data recollected in the study area.

The development of a quantitative structural model based on Aerial Laser Scanning (ALS) point clouds was proposed to accurately estimate tree attributes automatically and to detect changes in forest structure. Results of statistical analysis of point cloud show the possibility to use UAV LiDAR data to characterize changes in the structure of vegetation.