Purpose of the Review: Many LiDAR remote sensing studies over the past decade promised data fusion as a potential avenue to increase accuracy, spatial-temporal resolution, and information extraction in the final data products. Here, we performed a structured literature review to analyze relevant studies on these topics published in the last decade and the main motivations and applications for fusion, and the methods used. We discuss the findings with a panel of experts and report important lessons, main challenges, and future directions. Recent Findings: LiDAR fusion with other datasets, including multispectral, hyperspectral, and radar, is found to be useful for a variety of applications in the literature, both at individual tree level and at area level, for tree/crown segmentation, aboveground biomass assessments, canopy height, tree species identification, structural parameters, and fuel load assessments etc. In most cases, gains are achieved in improving the accuracy (e.g. better tree species classifications), and spatial-temporal resolution (e.g. for canopy height). However, questions remain regarding whether the marginal improvements reported in a range of studies are worth the extra investment, specifically from an operational point of view. We also provide a clear definition of “data fusion” to inform the scientific community on data fusion, combination, and integration. Summary: This review provides a positive outlook for LiDAR fusion applications in the decade to come, while raising questions about the trade-off between benefits versus the time and effort needed for collecting and combining multiple datasets.

Forest inventory parameters play an important role in understanding various biophysical processes of forest ecosystems. The present study aims at integrating Terrestrial Laser Scanner (TLS) and ALOS PALSAR L-band Synthetic Aperture Radar (SAR) data to assess Aboveground Biomass (AGB) in the Barkot Forest Range, Uttarakhand, India. The integration was performed to overcome the AGB saturation issue in ALOS PALSAR L-band SAR data for the high biomass density forest of the study area using 13 plots. Various parameters, namely, Gray-Level Co-Occurrence Matrix (GLCM) texture measures, Yamaguchi decomposition components, polarimetric parameters, and backscatter values of HH and HV band intensity, were derived from the ALOS SAR data. However, TLS was used to obtain the diameter at breast height (dbh) and tree height for the sample plots. A total of 23 parameters was retrieved using TLS and SAR data for integration with the LiDAR footprint. The integration was performed using Random Forest (RF) and Artificial Neural Network (ANN). The statistical measures for RF were found to be promising compared with ANN for AGB estimation. The R² value obtained for the RF was 0.94, with an RMSE of 59.72 ton ha⁻¹ for the predicted biomass value. The RMSE% was 15.92, while the RMSE_CV was 0.15. The R² value for ANN was 0.77, with an RMSE of 98.46 ton ha⁻¹. The RMSE% was 26.0, while the RMSE_CV was 0.26. RF performed better in estimating the biomass, which ranged from 122.46 to 581.89 ton ha⁻¹, while uncertainty ranged from 15.75 to 85.14 ton ha⁻¹. The integration of SAR and LiDAR data using machine learning shows great potential in overcoming AGB saturation of SAR data.

Purpose of Review: In recent years, the use of 3D point clouds in silviculture and forest ecology has seen a large increase in interest. With the development of novel 3D capture technologies, such as laser scanning, an increasing number of algorithms have been developed in parallel to process 3D point cloud data into more tangible results for forestry applications. From this variety of available algorithms, it can be challenging for users to decide which to apply to fulfil their goals best. Here, we present an extensive overview of point cloud acquisition and processing tools as well as their outputs for precision forestry. We then provide a comprehensive database of 24 algorithms for processing forest point clouds obtained using close-range techniques, specifically ground-based platforms. Recent Findings: Of the 24 solutions identified, 20 are open-source, two are free software, and the remaining two are commercial products. The compiled database of solutions, along with the corresponding technical guides on installation and general use, is accessible on a web-based platform as part of the COST Action 3DForEcoTech. The database may serve the community as a single source of information to select a specific software/algorithm that works for their requirements. Summary: We conclude that the development of various algorithms for processing point clouds offers powerful tools that can considerably impact forest inventories in the future, although we note the necessity of creating a standardisation paradigm.

In forestry research, for forest inventories or other applications which require accurate 3D information on the forest structure, a Terrestrial Laser Scanner (TLS) is an efficient tool for vegetation structure estimation. Light Detection and Ranging (LiDAR) can even provide high-resolution information in tree canopies due to its high penetration capability. Depending on the forest plot size, tree density, and structure, multiple TLS scans are acquired to cover the forest plot in all directions to avoid any voids in the dataset that are generated. However, while increasing the number of scans, we often tend to increase the data redundancy as we keep acquiring data for the same region from multiple scan positions. In this research, an extensive qualitative analysis was carried out to examine the capability and efficiency of TLS to generate canopy top points in six different scanning combinations. A total of nine scans were acquired for each forest plot, and from these nine scans, we made six different combinations to evaluate the 3D vegetation structure derived from each scan combination, such as Center Scans (CS), Four Corners Scans (FCS), Four Corners with Center Scans (FCwCS), Four Sides Center Scans (FSCS), Four Sides Center with Center Scans (FSCwCS), and All Nine Scans (ANS). We considered eight forest plots with dimensions of 25 m × 25 m, of which four plots were of medium tree density, and the other four had a high tree density. The forest plots are located in central Slovakia; European beech was the dominant tree species with a mixture of European oak, Silver fir, Norway spruce, and European hornbeam. Altogether, 487 trees were considered for this research. The quantification of tree canopy top points obtained from a TLS point cloud is very crucial as the point cloud is used to derive the Digital Surface Model (DSM) and Canopy Height Model (CHM). We also performed a statistical evaluation by calculating the differences in the canopy top points between ANS and the five other combinations and found that the most significantly different combination was FSCwCS respective to ANS. The Root Mean Squared Error (RMSE) of the deviations in tree canopy top points obtained for plots TLS_Plot1 and TLS_Plot2 ranged from 0.89 m to 14.98 m and 0.61 m to 7.78 m, respectively. The relative Root Mean Squared Error (rRMSE) obtained for plots TLS_Plot1 and TLS_Plot2 ranged from 0.15% to 2.48% and 0.096% to 1.22%, respectively.

Recently, the cultivation of fast-growing tree (FGT) plantations has gained importance due to the growing energy and climate crisis. FGT plantations have the potential to reduce carbon footprints and lower greenhouse gas emissions by utilization of local renewable energy sources. Effective monitoring of above-ground biomass (AGB) is crucial for the successful management of these plantations. Standard methods for estimating AGB rely on easily measurable parameters, such as Diameter at Breast Height (DBH) and tree height, which are highly correlated with AGB. Traditional methods for measuring DBH include measuring tapes and calipers; however, these techniques can be labor-intensive, time-consuming, and limited when assessing large areas. Innovative approaches, such as photogrammetry, terrestrial laser scanning (TLS), mobile laser scanning (MLS), and iPhone LiDAR scanning, can complement these traditional methods by generating point clouds that can be used for extracting dendrometric parameters. This study evaluates the effectiveness of TLS (RIEGL VZ-1000), MLS (Stonex X120 GO), iPhone LiDAR (iPhone 13 Pro MAX), and terrestrial photogrammetry (iPhone 13 Pro MAX) for estimating DBH in a Paulownia plantation. Each technology has limitations: while TLS offers high accuracy, it is also expensive and time-consuming. Similarly, MLS is relatively costly. On the other hand, iPhone LiDAR and terrestrial photogrammetry are more affordable alternatives; however, the iPhone LiDAR has a limited scanning range, and photogrammetry requires considerable time and expertise for data collection and processing. The primary objective of this study was to evaluate these technologies based on their accuracy in DBH estimation, ease of use, data collection, processing time, and cost within the ideal conditions of a Paulownia plantation (characterized by the absence of understory, level ground, and uniform tree shape and spacing). The aim was to determine whether traditional methods could be replaced with more efficient, quicker, easier, and cost-effective alternatives. Results indicated that TLS, MLS, and photogrammetry provided similar DBH estimation accuracies, with root mean square error (RMSE) values between 0.7 and 0.72 cm and relative RMSE values between 2.87 % and 2.95 %. In contrast, the iPhone LiDAR was the least accurate, with an RMSE of 1.7 cm and an rRMSE of 6.96 %. This study demonstrates that all evaluated technologies offer sufficient accuracy for DBH estimation, although TLS and MLS capture additional parameters at a higher cost. Therefore, TLS is impractical for DBH estimation in plantation environments due to its high cost, time, and labor demands. While less expensive, terrestrial photogrammetry also requires significant time investment and operator expertise. Despite its cost, MLS achieved the best results among all the evaluated technologies and proved to be the fastest and relatively simple. If cost is a concern, the best solution for DBH estimation in an FGT plantation environment would be iPhone LiDAR scanning. It represents the most affordable option with satisfactory accuracy and ease of use.This abstract is based upon work from COST Action 3DForEcoTech, CA20118, supported by COST (European Cooperation in Science and Technology) and APVV 20 0391 Monitoring of forest stands in three-dimensional space and time by innovative close-range approaches.