Geomatic Data Fusion for 3D Tree Modeling: The Case Study of Monumental Chestnut Trees

Abstract

In recent years, advancements in remote and proximal sensing technology have driven innovation in environmental and land surveys. The integration of various geomatics devices, such as reflex and UAVs equipped with RGB cameras and mobile laser scanners (MLS), allows detailed and precise surveys of monumental trees. With these data fusion method, we reconstructed three monumental 3D tree models, allowing the computation of tree metric variables such as diameter at breast height (DBH), total height (TH), crown basal area (CBA), crown volume (CV) and wood volume (WV), even providing information on the tree shape and its overall conditions. We processed the point clouds in software such as CloudCompare, 3D Forest, R and MATLAB, whereas the photogrammetric processing was conducted with Agisoft Metashape. Three-dimensional tree models enhance accessibility to the data and allow for a wide range of potential applications, including the development of a tree information model (TIM), providing detailed data for monitoring tree health, growth, biomass and carbon sequestration. The encouraging results provide a basis for extending the virtualization of these monumental trees to a larger scale for conservation and monitoring.

Charting the Research Terrain for Large Old Trees: Findings from a Quantitative Bibliometric Examination in the Twenty-First Century

“…High-resolution hyperspectral and thermal imaging enables noninvasive monitoring of ecophysiological stress and resilience traits [51]. Point clouds generated from LiDAR and photogrammetry can be used to quantify morphological parameters indicating tree vigor and disturbance responses [72]. Online databases and mobile apps allow for the collaborative mapping and tracking of heritage trees by professionals and community scientists.…”

A Handheld Laser-Scanning-Based Methodology for Monitoring Tree Growth in Chestnut Orchards

“…High-resolution UAV images have automatically identified chestnut fruits and estimated production and yield [ 18 ]. HHLS has also very recently been used by Balestra et al [ 19 ] to model monumental chestnut trees. MLS, based on HHLS with an integrated simultaneous localization and mapping (SLAM) algorithm, offers exceptional flexibility and mobility, overcoming some problems associated with the use of multi-scan TLS methods.…”

Evaluating the Point Cloud of Individual Trees Generated from Images Based on Neural Radiance Fields (NeRF) Method

“…Huang et al used a consumer-grade handheld camera to acquire images of a desert plant and were able to reconstruct a point-cloud model with accuracy comparable to that of from TLS [5]; Kükenbrink et al [6] evaluated the performance of an action camera (GoPro) combined with the Structure from Motion (SfM) [7] technique to acquire 3D point clouds of trees, and their point cloud model was of similar quality to that of Lidar devices (TLS, MLS and UAV-laser scanning) in terms of the DBH extraction results, but still fell short of the performance in other aspects. For this reason, many scholars have opened up new ideas to acquire forest point cloud data using a portable camera and a backpack laser scanner, combining these two types of data to extract tree height and DBH [8]; Balestra et al fused the data from a RGB camera, UAV camera and mobile laser scanner to reconstruct three giant chestnut tree models, providing researchers with accurate information about the shape and overall condition of the trees [9]. Although these photogrammetry-based 3D tree reconstruction methods are simple, automated and effective, the reconstruction accuracy is greatly affected by the fact that the trees themselves have complex self-similar branch morphological structures and the inner branches are shaded by leaves.…”