Assessing the Performance of a Handheld Laser Scanning System for Individual Tree Mapping—A Mixed Forests Showcase in Spain

Abstract

The use of mobile laser scanning to survey forest ecosystems is a promising, scalable technology to describe the 3D structure of forests at a high resolution. We use a structurally complex, mixed-species Mediterranean forest to test the performance of a mobile Handheld Laser Scanning (HLS) system to estimate tree attributes within a forest patch in central Spain. We describe the different stages of the HLS approach: field position, ground data collection, scanning path design, point cloud processing, alignment between detected trees and measured reference trees, and finally, the assessment of main tree structural attributes diameter at breast height (DBH) and tree height considering species and tree size as control factors. We surveyed 418 reference trees to account for omission and commission error rates over a 1 ha plot divided into 16 sections and scanned using two different scanning paths. The HLS-based approach reached a high of 88 and 92% tree detection rate for the best combination of scanning path and point cloud processing modes for the HLS system. The root mean squared errors for DBH estimates varied between species: errors for Pinus pinaster were below 2 cm for Scan 02. Quercus pyrenaica, and Alnus glutinosa showed higher error rates. We observed good agreement between ALS and HLS estimates for tree height, highlighting differences to field measurements. Despite the complexity of the mixed forest area surveyed, our results show that HLS is highly efficient at detecting tree locations, estimating DBH, and supporting tree height measurements as confirmed with airborne laser data used for validation. This study is one of the first HLS-based studies conducted in the Mediterranean mixed forest region, where variability in tree allometries and spacing and the presence of natural regeneration pose challenges for the HLS approach. HLS is a feasible, time-efficient, scalable technology for tree mapping in mixed forests with potential to support forest monitoring programmes such as national forest inventories lacking three-dimensional, remote sensing data to support field measurements.

Advances in Vegetation Structure Modelling Using Remote Sensing to Support the Acquisition of Sustainable Development Goals through Forest Management

“…Forest ecosystems provide a wide variety of services from timber to non-timber products that can be predicted with remote sensing tools to identify management options while promoting sustainable uses of forests. Tupinamba et al [4] assessed the technical configuration parameters of the handheld laser scanner (scanning path and point cloud transformation modes including rigid and non-rigid) to demonstrate its efficiency for forest inventory purposes (tree detection, diameter, and height) in a Mediterranean mixed forest composed of Pinus pinaster, Quercus pyrenaica, and Alnus glutinosa. Martinez-Rodrigo et al [5] proposed a multi-source approach based on climate data, Landsat images, field data, and a mobile terrestrial laser scanner to analyse the influence of climatic, primary productivity, and structural drivers (biomass, basal area, or stand density) on triggering mushroom fruiting in Mediterranean Pinus pinaster.…”

“…The remaining six contributions use only one remote sensing sensor. For example, the terrestrial handheld laser scanner constitutes a useful data source for individual tree detection (detection rates of 88 and 92%), tree diameter, and tree height (relative RMSE below 10%) [4], and similarly, the terrestrial laser scanner is a suitable choice for volume or canopy cover prediction [5]. Though the visualization geometry of terrestrial systems may slightly underestimate some metrics (i.e., height), it is more precise than field measurements in heterogeneous stands.…”

“…In particular, four contributions used machine learning algorithms, such as random forest [7,8,10], for classification and forest change analysis, and a support vector machine [11] for selecting edges in tree crown delineation. Furthermore, parametric models, such as linear regression, were used for forest inventory metric prediction [4,10], generalized linear models for microclimate prediction [6], and generalized additive mixed models, which combine the flexibility of generalized additive models while accounting for random effects (i.e., plot), were used for predicting yield mushrooms. The choice of different algorithms may be linked to dataset size, feasibility, or even the application, but should be parsimonious and pursue a biological meaning [12], so as to enhance sustainable forest management.…”

Monitoring Solutions for Remote Locations: A Data Gathering Approach for Remote Nature-Based Solution Sites

“…They are then integrated into the photogrammetric process to improve accuracy [36]. GCPs/GPS are versatile tools, useful for purposes such as geo-referencing handheld LiDAR data, validating other collected elevation data, adhering to site design specifications, and determining embankment heights [37,38].…”

Assessing the Performance of Handheld Laser Scanning for Individual Tree Mapping in an Urban Area

“…In recent decades, close-range remote sensing has undergone rapid development, fundamentally altering the landscape of in situ forest inventories [8]. Three-dimensional point cloud data obtained through light detection and ranging (LiDAR) technology are currently being utilized for the precise extraction of forest characteristics, such as individual tree position, DBH, tree height, and forest biomass [7,[13][14][15][16][17][18].…”