Search Results (107)

This study examined the integraftion of airborne Light Detection and Ranging (LiDAR), Simultaneous Localization and Mapping (SLAM)-based handheld LiDAR, and iPhone LiDAR to inspect forest road networks following forest operations. The goal is to overcome the challenges posed by dense canopy cover and ensure accurate and efficient data collection and mapping. Airborne data were collected using the DJI Matrice 300 RTK UAV equipped with a Zenmuse L2 LiDAR sensor, which achieved a high point density of 285 points/m² at an altitude of 80 m. Ground-level data were collected using the BLK2GO handheld laser scanner (HPLS) with SLAM methods (LiDAR SLAM, Visual SLAM, Inertial Measurement Unit) and the iPhone 13 Pro Max LiDAR. Data processing included generating DEMs, DSMs, and True Digital Orthophotos (TDOMs) via DJI Terra, LiDAR360 V8, and Cyclone REGISTER 360 PLUS, with additional processing and merging using CloudCompare V2 and ArcGIS Pro 3.4.0. The pairwise comparison analysis between ALS data and each alternative method revealed notable differences in elevation, highlighting discrepancies between methods. ALS + iPhone demonstrated the smallest deviation from ALS (MAE = 0.011, RMSE = 0.011, RE = 0.003%) and HPLS the larger deviation from ALS (MAE = 0.507, RMSE = 0.542, RE = 0.123%). The findings highlight the potential of fusing point clouds from diverse platforms to enhance forest road mapping accuracy. However, the selection of technology should consider trade-offs among accuracy, cost, and operational constraints. Mobile LiDAR solutions, particularly the iPhone, offer promising low-cost alternatives for certain applications. Future research should explore real-time fusion workflows and strategies to improve the cost-effectiveness and scalability of multisensor approaches for forest road monitoring. Full article

The rapid measurement of three-dimensional bridge geometric shapes is crucial for assessing construction quality and in-service structural conditions. Existing geometric shape measurement methods predominantly rely on traditional surveying instruments, which suffer from low efficiency and are limited to sparse point sampling. This study proposes a novel framework that utilizes an airborne LiDAR–camera fusion system for data acquisition, reconstructs high-precision 3D bridge models through real-time mapping, and automatically extracts structural geometric shapes using deep learning. The main contributions include the following: (1) A synchronized LiDAR–camera fusion system integrated with an unmanned aerial vehicle (UAV) and a microprocessor was developed, enabling the flexible and large-scale acquisition of bridge images and point clouds; (2) A multi-sensor fusion mapping method coupling visual-inertial odometry (VIO) and Li-DAR-inertial odometry (LIO) was implemented to construct 3D bridge point clouds in real time robustly; and (3) An instance segmentation network-based approach was proposed to detect key structural components in images, with detected geometric shapes projected from image coordinates to 3D space using LiDAR–camera calibration parameters, addressing challenges in automated large-scale point cloud analysis. The proposed method was validated through geometric shape measurements on a concrete arch bridge. The results demonstrate that compared to the oblique photogrammetry method, the proposed approach reduces errors by 77.13%, while its detection time accounts for 4.18% of that required by a stationary laser scanner and 0.29% of that needed for oblique photogrammetry. Full article

We applied UAV (Unmanned Aerial Vehicle) and ALS (Airborne Laser Scanning) remote sensing methods to identify terrain obstacles encountered during timber extraction in the skidding process with the aim of proposing accessibility solutions to the inner parts of forest stands using skidding trails. At the Vítovický žleb site, located east of Brno in the South Moravian Region of the Czech Republic, we analysed the accuracy of digital terrain models (DTMs) created from UAV LiDAR (Light Detection and Ranging), RGB (Red–Green–Blue) UAV, ALS data taken on site and publicly available LiDAR data DMR 5G (Digital Model of Relief of the Czech Republic, 5th Generation, based on airborne laser scanning, providing pre-classified ground points with an average density of 1 point/m²). UAV data were obtained using two types of drones: a DJI Mavic 2 mounted with an RGB photogrammetric camera and a GeoSLAM Horizon laser scanner on a DJI M600 Pro hexacopter. We achieved the best accuracy with UAV technologies, with an average deviation of 0.06 m, compared to 0.20 m and 0.71 m for ALS and DMR 5G, respectively. The RMSE (Root Mean Square Error) values further confirm the differences in accuracy, with UAV-based models reaching as low as 0.71 m compared to over 1.0 m for ALS and DMR 5G. The results demonstrated that UAVs are well-suited for detailed analysis of rugged terrain morphology and obstacle identification during timber extraction, potentially replacing physical terrain surveys for timber extraction planning. Meanwhile, ALS and DMR 5G data showed significant potential for use in planning the placement of skidding trails and determining the direction and length of timber extraction from logging sites to forest roads, primarily due to their ability to cover large areas effectively. Differences in the analysis results obtained using GIS (Geographic Information System) cost surface solutions applied to ALS and DMR 5G data DTMs were evident on logging sites with terrain obstacles, where the site-specific ALS data proved to be more precise. While DMR 5G is based on ALS data, its generalised nature results in lower accuracy, making site-specific ALS data preferable for analysing rugged terrain and planning timber extractions. However, DMR 5G remains suitable for use in more uniform terrain without obstacles. Thus, we recommend combining UAV and ALS technologies for terrain with obstacles, as we found this approach optimal for efficiently planning the logging-transport process. Full article

Accurate estimates of aboveground biomass (AGB) are essential for forest policies to reduce carbon emissions. Unmanned aerial laser scanning (UAV-LS) offers unprecedented millimetric detail but is underutilized in monitoring broadleaf Mediterranean forests compared to coniferous ones. This study aims to design and evaluate a procedure for AGB estimates based on the predictive power of crown features. In the first step, we manually created Quantitative Structure Models (QSMs) for 320 trees using data from UAV laser scanning (UAV-LS), airborne laser scanning (ALS), and co-registered terrestrial laser scanning (TLS). This provided the most accurate non-destructive estimate of aboveground biomass (AGB) in the absence of destructive measurements. For each reference tree we also measured crown projection and crown volume to build two separated models relating AGB to such crown features. In the second phase, we evaluated the potential of UAV-LS for quantifying AGB in a pure European beech (Fagus sylvatica) forest and compared it with traditional ALS estimates, using fully automatic procedures. The two obtained tree-level AGB models were then tested using three datasets derived from 35 sampling plots over the same study area: (a) 1130 trees manually segmented (phase-2 reference); (b) trees automatically extracted from ALS data; and (c) trees automatically extracted from UAV-LS data. Results demonstrate that detailed UAV-LS data improve model sensitivity compared to ALS data (RMSE = 45.6 Mg ha⁻¹, RMSE% = 13.4%, R2 = 0.65, for the best ALS model; RMSE = 44.0 Mg ha⁻¹, RMSE% = 12.9%, R2 = 0.67, for the best UAV-LS model), allowing for the detection of AGB differences even in quite homogenous forest structures. Overall, this study demonstrates the combined use of both laser scanner data can foster non-destructive and more precise AGB estimation than the use of only one, in forested areas across hectare scales (1 to 100 ha). Full article

Quantifying forest structure to assess changing wildfire risk factors is critical as vulnerable areas require mitigation, management, and resource allocation strategies. Remote sensing offers the opportunity to accurately measure forest attributes without time-intensive field inventory campaigns. Here, we quantified forest canopy cover and individual tree metrics across 44 plots (20 m × 20 m) in oak woodlands and mixed-conifer forests in Northern California using structure-from-motion (SfM) 3D point clouds derived from unoccupied aerial systems (UAS) multispectral imagery. In addition, we compared UAS–SfM estimates with those derived using similar methods applied to Airborne Laser Scanning (ALS) 3D point clouds as well as traditional ground-based measurements. Canopy cover estimates were similar across remote sensing (ALS, UAS-SfM) and ground-based approaches (r² = 0.79, RMSE = 16.49%). Compared to ground-based approaches, UAS-SfM point clouds allowed for correct detection of 68% of trees and estimated tree heights were significantly correlated (r² = 0.69, RMSE = 5.1 m). UAS-SfM was not able to estimate canopy base height due to its inability to penetrate dense canopies in these forests. Since canopy cover and individual tree heights were accurately estimated at the plot-scale in this unique bioregion with diverse topography and complex species composition, we recommend UAS-SfM as a viable approach and affordable solution to estimate these critical forest parameters for predictive wildfire modeling. Full article

This study reviews research from 2010 to 2023 on the integration of airborne laser scanning (ALS) metrics with satellite and ground-based data for forest monitoring, highlighting the potential of the combined use of ALS and optical remote sensing data in improving the accuracy and the frequency. Following an in-depth screening process, 42 peer-reviewed scientific manuscripts were selected and comprehensively analyzed, identifying how the integration among different sources of information facilitate frequent, large-scale updates, crucial for monitoring forest ecosystems dynamics and changes, aiding in supporting sustainable management and climate smart forestry. The results showed how ALS metrics—especially those related to height and intensity—improved estimates precision of forest volume, biomass, biodiversity, and structural attributes, even in dense vegetation, with an R² up to 0.97. Furthermore, ALS data were particularly effective for monitoring urban forest variables (R² 0.83–0.92), and for species classification (overall accuracy up to 95%), especially when integrated with multispectral and hyperspectral imagery. However, our review also identified existing challenges in predicting biodiversity variables, highlighting the need for continued methodological improvements. Importantly, while some studies revealed great potential, novel applications aiming at improving ALS-derived information in spatial and temporal coverage through the integration of optical satellite data were still very few, revealing a critical research gap. Finally, the ALS studies’ distribution was extremely biased. Further research is needed to fully explore its potential for global forest monitoring, particularly in regions like the tropics, where its impact could be significant for ecosystem management and conservation. Full article

The presence of sufficient natural regeneration in mature forests is regarded as a pivotal criterion for their future stability, ensuring seamless reforestation following final harvesting operations or forest calamities. Consequently, forest regeneration is typically quantified as part of forest inventories to monitor its occurrence and development over time. Light detection and ranging (LiDAR) technology, particularly ground-based LiDAR, has emerged as a powerful tool for assessing typical forest inventory parameters, providing high-resolution, three-dimensional data on the forest structure. Therefore, it is logical to attempt a LiDAR-based quantification of forest regeneration, which could greatly enhance area-wide monitoring, further supporting sustainable forest management through data-driven decision making. However, examples in the literature are relatively sparse, with most relevant studies focusing on an indirect quantification of understory density from airborne LiDAR data (ALS). The objective of this study is to develop an accurate and reliable method for estimating regeneration coverage from data obtained through personal laser scanning (PLS). To this end, 19 forest inventory plots were scanned with both a personal and a high-resolution terrestrial laser scanner (TLS) for reference purposes. The voxelated point clouds obtained from the personal laser scanner were converted into raster images, providing either the canopy height, the total number of filled voxels (containing at least one LiDAR point), or the ratio of filled voxels to the total number of voxels. Local maxima in these raster images, assumed to be likely to contain tree saplings, were then used as seed points for a raster-based tree segmentation, which was employed to derive the final regeneration coverage estimate. The results showed that the estimates differed from the reference in a range of approximately −10 to +10 percentage points, with an average deviation of around 0 percentage points. In contrast, visually estimated regeneration coverages on the same forest plots deviated from the reference by between −20 and +30 percentage points, approximately −2 percentage points on average. These findings highlight the potential of PLS data for automated forest regeneration quantification, which could be further expanded to include a broader range of data collected during LiDAR-based forest inventory campaigns. Full article

Reduced-impact logging (RIL) has been recognized as a promising strategy for biodiversity conservation and carbon sequestration within sustainable forest management (SFM) areas. However, monitoring the forest understory—a critical area for assessing logging impacts—remains challenging due to limitations in conventional methods such as field inventories and global navigation satellite system (GNSS) surveys, which are time-consuming, costly, and often lack accuracy in complex environments. Additionally, aerial and satellite imagery frequently underestimate the full extent of disturbances as the forest canopy obscures understory impacts. This study examines the effectiveness of the relative density model (RDM), derived from airborne LiDAR data, for mapping and monitoring understory disturbances. A field-based validation of LiDAR-derived RDM was conducted across 25 sites, totaling 5504.5 hectares within the Jamari National Forest, Rondônia, Brazil. The results indicate that the RDM accurately delineates disturbances caused by logging infrastructure, with over 90% agreement with GNSS field data. However, the model showed the greatest discrepancy for skid trails, which, despite their lower accuracy in modeling, accounted for the largest proportion of the total impacted area among infrastructure. The findings include the mapping of 35.1 km of primary roads, 117.4 km of secondary roads, 595.6 km of skid trails, and 323 log landings, with skid trails comprising the largest proportion of area occupied by logging infrastructure. It is recommended that airborne LiDAR assessments be conducted up to two years post-logging, as impacts become less detectable over time. This study highlights LiDAR data as a reliable alternative to traditional monitoring approaches, with the ability to detect understory impacts more comprehensively for monitoring selective logging in SFM areas of the Amazon, providing a valuable tool for both conservation and climate mitigation efforts. Full article

Currently, many digital elevation models (DEMs) are derived from airborne LiDAR data acquisition flights. The vertical accuracy of both products has typically been evaluated using methods based on randomly sampled control points. However, due to the superficial nature of the DEM, logic suggests that it is more appropriate to use a superficial object as an evaluation and control element, that is, a “control surface” or “control patch”. Our approach proposes a method for obtaining each patch from a georeferenced point cloud (PC) measured with a terrestrial laser scanner (TLS). In order to reduce the dilution of precision due to very acute angles of incidence that occur between the terrain and the scanner′s rays when it is stationed on a conventional tripod, a system has been created that allows the scanner to be placed face down at a height of up to 7 m. Stationing the scanner at that height also has the advantage of reducing shadow areas in the presence of possible obstacles. In our experiment, the final result is an 18 m × 18 m PC patch which, after resampling, can be transformed into a high-density (10,000 points/m²) and high-quality (absolute positional uncertainty < 0.05 m) DEM patch, that is, with a regular mesh format. This DEM patch can be used as the ground truth to assess the surface accuracy of DEMs (DEM format) or airborne LiDAR data acquisition flights (PC format). Full article

Automating forest machines to optimize the forest value chain requires the ability to map the surroundings of the machine and to conduct accurate measurements of nearby trees. In the near-to-medium term, integrating a forest harvester with a mobile laser scanner system may have multiple applications, including real-time assistance of the harvester operator using laser-scanner-derived tree measurements and the collection of vast amounts of training data for large-scale airborne laser scanning-based surveys at the individual tree level. In this work, we present a comprehensive processing flow for a mobile laser scanning (MLS) system mounted on a forest harvester starting from the localization of the harvester under the forest canopy followed by accurate and automatic estimation of tree attributes, such as diameter at breast height (DBH) and stem curve. To evaluate our processing flow, we recorded and processed MLS data from a commercial thinning operation on three test strips with a total driven length ranging from 270 to 447 m in a managed Finnish spruce forest stand containing a total of 658 reference trees within a distance of 15 m from the harvester trajectory. Localization reference was obtained by a robotic total station, while reference tree attributes were derived using a high-quality handheld laser scanning system. As some applications of harvester-based MLS require real-time capabilities while others do not, we investigated the positioning accuracy both for real-time localization of the harvester and after the optimization of the full trajectory. In the real-time positioning mode, the absolute localization error was on average 2.44 m, while the corresponding error after the full optimization was 0.21 m. Applying our automatic stem diameter estimation algorithm for the constructed point clouds, we measured DBH and stem curve with a root-mean-square error (RMSE) of 3.2 cm and 3.6 cm, respectively, while detecting approximately 90% of the reference trees with $\mathrm{DBH}>20$ cm that were located within 15 m from the harvester trajectory. To achieve these results, we demonstrated a distance-adjusted bias correction method mitigating diameter estimation errors caused by the high beam divergence of the laser scanner used. Full article

The albedo of boreal forests depends on the properties of the forest and is a key parameter for understanding the climate impact of forest management practices at high northern latitudes. While high-resolution albedo retrievals from satellites remain challenging, unmanned aerial vehicles (UAVs) offer the ability to obtain albedo corresponding to the typical size of forest stands or even smaller areas, such as forest plots. Plots and pixels of sizes in the typical range of 200–400 m² are used as the basic units in forest management in the Nordic countries. In this study, the aim was to evaluate the effect of the differences in the footprint size of the measured albedo and fixed-area forest plots on the relationship between albedo and forest attributes. This was performed by examining the correlation between albedo and field-measured forest attributes and metrics derived from airborne laser scanner data using linear regression models. The albedo was measured by a UAV above 400 m², circular forest plots (n = 128) for seven different flight heights above the top of the canopy. The flight heights were chosen so the plots were always smaller than the footprint of the measured albedo, and the area of a forest plot constituted 30–90% of the measured albedo. The applied pyranometer aboard the UAV measured the albedo according to a cosine response across the footprint. We found the strongest correlation when there was the greatest correspondence between the spatial size of the albedo footprint and the size of the forest plots, i.e., when the target area constituted 80–90% of the measured albedo. The measured albedo of the plots in both regeneration forests and mature forests were highly sensitive (p-values ≤ 0.001) to the footprint size, with a mean albedo difference of 11% between the smallest and largest footprints. The mean albedo of regeneration forests was 33% larger than that of mature forests for footprint sizes corresponding to 90%. The study demonstrates the importance of corresponding spatial sizes of albedo measurements and the target areas subject to measurements. Full article

Hazards associated with natural factors annually result in significant human and economic losses. An accurate and up-to-date assessment of various hazards can limit their impact and bring benefits both in the modeling phase and mostly in the risk mitigation plan stage. The article presents the results of a multi-hazard analysis that considers floods, landslides, and earthquakes carried out in the Ungheni area, located in the eastern part of Romania at the border with the Republic of Moldova. The research focused on producing harmonized hazard maps for the two countries since the area spreads jointly between the two countries. Common geospatial data were used for modeling and risk assessment, such as airborne laser scanners, global navigation satellite systems, rasters, and vectors from analog and digital sources. Among hazards, the flood maps for the studied area, Ungheni, were designed using 2D hydraulic modeling in HECRAS software (version 6.3.1); the landslide maps considered the ArcGis platform following Romanian methodology; and the seismic analysis collected onsite measurements on the built environment. The shared use of geospatial data in modeling the three hazards led to high accuracy of the results and determined their spatial homogeneity. It was observed that only two areas, Mînzătești and Coada Stîncii villages from Ungheni Areal, are highly vulnerable to all three hazards. The research findings, along with mitigation recommendations, have contributed to the development of a more precise action plan for natural hazards events by local authorities and decision-makers. Full article

The use of mobile laser scanning for mapping forests has scarcely been studied in difficult forest conditions. In this paper, we compare the accuracy of retrieving tree attributes, particularly diameter at breast height (DBH), stem curve, stem volume, and tree height, using six different laser scanning systems in a managed natural boreal forest. These compared systems operated both under the forest canopy on handheld and unmanned aerial vehicle (UAV) platforms and above the canopy from a helicopter. The complexity of the studied forest sites ranged from easy to difficult, and thus, this is the first study to compare the performance of several laser scanning systems for the direct measurement of stem curve in difficult forest conditions. To automatically detect tree stems and to calculate their attributes, we utilized our previously developed algorithm integrated with a novel bias compensation method to reduce the overestimation of stem diameter arising from finite laser beam divergence. The bias compensation method reduced the absolute value of the diameter bias by 55–99%. The most accurate laser scanning systems were equipped with a Velodyne VLP-16 sensor, which has a relatively low beam divergence, on a handheld or UAV platform. In easy plots, these systems found a root-mean-square error (RMSE) of below 10% for DBH and stem curve estimates and approximately 10% for stem volume. With the handheld system in difficult plots, the DBH and stem curve estimates had an RMSE under 10%, and the stem volume RMSE was below 20%. Even though bias compensation reduced the difference in bias and RMSE between laser scanners with high and low beam divergence, the RMSE remained higher for systems with a high beam divergence. The airborne laser scanner operating above the forest canopy provided tree attribute estimates close to the accuracy of the under-canopy laser scanners, but with a significantly lower completeness rate for stem detection, especially in difficult forest conditions. Full article

Thinning focused on achieving growth and diameter management objectives has typically led to stands with reduced climate sensitivity compared to unthinned stands. We integrated dendrochronological with Airborne Laser Scanner (LiDAR) data and growth models to assess the long-term impact of thinning intensity on Canary pine (Pinus canariensis) radial growth. In 1988, 18 permanent treatment units were established in 73-year-old Canary pine plantations and three thinning treatments were applied (C–control-unthinned; 0% basal area removal; MT–moderate thinning: 10% and 15% basal area removal, and HT–heavy thinning: 46% and 45% basal area removal on the windward and leeward slopes, respectively). Dendrochronological data were measured in 2022 and expressed as basal area increment (BAI). The impact of climate on growth was examined by fitting linear regression models considering two different Representative Concentration Pathway (RCP) climate scenarios, RCP 2.6 and RCP 4.5. Finally, LiDAR data were used for standing segmentation to evaluate changes in overall growth under different climatic scenarios. The LiDAR–stand attributes differed between aspects. The BAI of the most recent 20 years (BAI₂₀) after thinning was significantly higher for the moderate and heavy treatments on the leeward plots (F = 47.31, p < 0.001). On the windward plots, BAI decreased after moderate thinning. Considerable thinning treatments resulted in stronger changes in growth when compared to RCP climatic scenarios. From a silviculture perspective, the mapping of canopy structure and growth response to thinning under different climatic scenarios provides managers with opportunities to conduct thinning strategies for forest adaptation. Combining dendrochronological and LiDAR data at a landscape scale substantially improves the value of the separate datasets as forecasted growth response maps allow improving thinning management plans. Full article

The exposure of Mediterranean forests to large wildfires requires mechanisms to prevent and mitigate their negative effects on the territory and ecosystems. Fuel models synthesize the complexity and heterogeneity of forest fuels and allow for the understanding and modeling of fire behavior. However, it is sometimes challenging to define the fuel type in a structurally heterogeneous forest stand due to the mixture of characteristics from the different types and limitations of qualitative field observations and passive and active airborne remote sensing. This can impact the performance of classification models that rely on the in situ identification of fuel types as the ground truth, which can lead to a mistaken prediction of fuel types over larger areas in fire prediction models. In this study, a handheld mobile laser scanner (HMLS) system was used to assess its capability to define Prometheus fuel types in 43 forest plots in Aragón (NE Spain). The HMLS system captured the vertical and horizontal distribution of fuel at an extremely high resolution to derive high-density three-dimensional point clouds (average: 63,148 points/m²), which were discretized into voxels of 0.05 m³. The total number of voxels in each 5 cm height stratum was calculated to quantify the fuel volume in each stratum, providing the vertical distribution of fuels (m³/m²) for each plot at a centimetric scale. Additionally, the fuel volume was computed for each Prometheus height stratum (0.60, 2, and 4 m) in each plot. The Prometheus fuel types were satisfactorily identified in each plot and were compared with the fuel types estimated in the field. This led to the modification of the ground truth in 10 out of the 43 plots, resulting in errors being found in the field estimation between types FT2–FT3, FT5–FT6, and FT6–FT7. These results demonstrate the ability of the HMLS systems to capture fuel heterogeneity at centimetric scales for the definition of fuel types in the field in Mediterranean forests, making them powerful tools for fuel mapping, fire modeling, and ultimately for improving wildfire prevention and forest management. Full article