Abstract
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.
Highlights
Forest constitutes the most biologically diverse terrestrial ecosystem on Earth and are imperative for maintaining the balance of terrestrial ecosystems (Dandois, Olano, and Ellis 2015)
Showed higher differences between Light Detection and Ranging (LiDAR) data and ground truth, being the Lidar height calculation higher than the ground truth collected by a vertex instrument
Except for herbaceous and shrub height of LiDAR data in plot 2, generally the LiDAR data was higher than the ground truth data
Summary
Forest constitutes the most biologically diverse terrestrial ecosystem on Earth and are imperative for maintaining the balance of terrestrial ecosystems (Dandois, Olano, and Ellis 2015). To promote and support sustainable forest management an accurate monitoring in timely fashion is required (Timilsina et al 2013). In this context, forest structural parameters (e.g., tree height, volume, and biomass) are key components for effectively quantifying forest structure and are vital for accurately monitoring forest dynamics (Fu et al 2021). LiDAR is a useful tool for the multi-dimensional characterization of forest structure because it has a strong capability to penetrate dense forest canopies and detect understory vegetation, thereby, obtaining high-precision threedimensional (3D) forest structure information. LiDAR data have been recently used to quantify complexity and diversity in vegetation structure in a successful way (Atkins et al 2018; Bakx et al 2019; Guo et al 2017; LaRue et al 2020)
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