AUTOMATED STRUCTURAL FOREST CHANGES USING LIDAR POINT CLOUDS AND GIS ANALYSES

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. * Corresponding author 1. INTRODUCCTION 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). Assessing changes in forest structure over time is crucial for monitoring forest resources. The generation of spatially explicit detailed maps of forest structure, and its dynamics, has multiple implications in forest managing wildfire risk reduction, carbon sequestration assessment, timber resources availability or wildfire habitat analysis may benefit from such high-resolution information. Forest structural diversity is the physical arrangement and variability of the living and non-living biotic elements within forest stands that support many essential ecosystem functions (LaRue et al. 2019). Forest structural diversity arises from the complex interactions of a range of abiotic and biotic factors that influence the growth and the quality of vegetation (Fotis et al. 2018). A wide variety of structural diversity metrics can be estimated using methods that range from traditional forest inventory approaches to nextgeneration remote sensing techniques (Fahey et al. 2019). The complex and dynamic nature of forest structure has proven to be challenging to measure accurately across scales and forest structure types (Atkins et al. 2018). The forestry management process is based on the use of a large amount of information which must be stored, managed, analyzed, simulated, and visualized in a dynamic and flexible way. Geographic Information Systems (GIS) and remote sensing are complementary technologies that, when combined, enable to improve monitoring, mapping and management of forest resources (S. E. Franklin 2001). Automation in the monitoring of the structural forest changes and forest mapping is one of the main aspects of applications of modern geoinformatics methods. The obtaining of objective information requires the use of spatial data derived from photogrammetry and remote sensing. Generating the spatial characteristics of vegetation in an automated way undoubtedly provides new possibilities in modelling the structure of vegetation, including defining biometric features and biomass, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B3-2021 XXIV ISPRS Congress (2021 edition) This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-603-2021 | © Author(s) 2021. CC BY 4.0 License. 603 which determines the developmental stage of trees and shrubs forming the succession process. Information about the spatial structure of vegetation provides a basis for studies of biodiversity, or spatial analyses that require up to date and precise information about land cover classes (Szostak 2020). Remote sensing has demonstrated its importance for the characterization of vegetation structure in sparsely dense forests, the greatest challenge being those with medium-high density (J. Franklin 2010). Light Detection and Ranging (LiDAR) is a remote sensing technology for characterizing the surface of the earth using a cloud of georeferenced points. 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. Over more, there are versatile terrestrial and aerial deployment platforms. Terrestrial laser scanning (TLS) and aerial laser scanning (ALS) have both been shown to be effective at quantifying components of forest structural diversity (LaRue et al. 2020). The development of airborne digital cameras and unmanned aerial vehicles (UAV) has promoted cost-effective methods for enhancing monitoring forest dynamics. In the forest sector, LiDAR has the potential to reduce the need for intensive ground-based measurement of stand structure, making it a valuable tool. 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). The present manuscript explores the accuracy of airborne LiDAR data to support, automatically map, and monitoring the forest structural changes, in a more efficient way than traditional forest methods do such as human field data collection. The aim of this work is to analyze the potential of automated mapping and detection changes in vegetation structure using LiDAR technology and Geographic Information System (GIS) analyses. In this way, human work would not be necessary for field data collection and the methodology could be extended to large or inaccessible areas, being able to map and analyze the vegetation evolution whose data are necessary in forest management. Firstly, the LiDAR data are collected with a year of difference between first and second flight. Moreover, the ground truth collected by human methods coincide with the second fly date. Then, data are recollected in two square plots of 2 m size within the study area. The LiDAR data are processed to be compared with ground truth data and the results accuracy is calculated in both study plots. Finally, the methodology is extended to the whole study area, and combustible fuel types and their evolution are mapped. In particular, the main contributions of this study are summarized as: Design of a methodology to group tree points by height, Prometheus classification, and heights established in ASPRS classification. Development of an algorithm to automatically calculate the height distribution of vegetation and their Canopy Cover Fraction (CCF). Calculate the accuracy of the methodology compared with the one for the ground truth. Analyse the forest changes in the whole study area, supported by hight parameters, biomass estimation, individual tree detection, CCF and fuel types of classification. 2. MATERIAL AND METHODS