Airborne Laser Scanning Data Research Articles

Predicting the roadway width of forest roads by means of airborne laser scanning

Modern forestry relies on an extensive network of well-maintained forest roads. However, the trafficability of forest roads can be limited by poor construction or by insufficient load-bearing capacity caused by, for example, weather conditions. In all forestry operations, the forest roads should be able to support heavy vehicles during the transport of roundwood and harvesting machinery, so logistics must be carefully planned to ensure good road trafficability. To facilitate this planning, it is necessary to know the various characteristics of the forest road in terms of its width, structure and load-bearing capacity. Obtaining such information using remote sensing data would be beneficial. In this study, we used airborne laser scanning (ALS) data to assess the width of the roadway on forest roads. We developed several algorithms that were used to assess roadway width based on the cross-sectional data (derived with ALS) of the roads in 8-m long segments. The ALS echoes within the segments were classified into 20 cm wide strips which the algorithms then analyzed in a stepwise manner. Both height and intensity information from the ALS data were utilized. The algorithm results were then used as predictors in linear models to predict the roadway width. The main focus was on the road level, i.e., aggregated values from 25 different roads were used in model fitting. In terms of root mean square error (RMSE) values, accuracies of approximately 20–30 cm (or 7–10 %) were obtained for the predicted roadway widths with a separate validation data. This level of accuracy can be considered adequate given the characteristics of the ALS data used in the modelling. The results indicate that the presented approach has potential for practical applications to predict the width attributes of forest roads.

Multi-resolution gridded maps of vegetation structure from GEDI

Large-extent maps of three-dimensional vegetation structure are important for understanding the hydrologic cycle, climate, carbon fluxes, and habitat. We aggregated over 7 billion lidar shots from the Global Ecosystem Dynamics Investigation (GEDI) to produce analysis-ready, gridded rasters of 36 vegetation structure metrics at three spatial resolutions (1, 6, and 12 km). We used 8 statistics to grid shots in every pixel, specifically the mean, bootstrapped standard error of the mean, median, standard deviation, interquartile range, Shannon’s Diversity Index, and shot count. We quantified uncertainty of the mean by randomly selecting 100 subsets of shots (i.e. bootstrapping) within each pixel. We also assessed the accuracy of several gridded metrics using fine spatial resolution airborne laser scanning data. The gridded metrics are generally more accurate at mid latitudes due to higher shot density and lower density of vegetation. Statistics associated with the central or maximum tendency of a metric are more accurate than statistics related to variability of metric values within the pixel.

Broadscale deep learning model for archaeological feature detection across the Maya area

Many Maya archaeological areas are not comprehensively or systematically mapped because ruins, often hidden under tropical forest canopy in rugged terrain, can take decades to locate, identify, and map. Recent years have seen an explosion of lidar data collection, and machine learning provides a way to exploit these lidar data, making feature analyses more efficient and consistently executed. At present, there are a limited number of small, area-specific models that exist for the Maya area, the largest of which covers 230 km2. Here we present the foundation for a broadscale, multi-area-based convolutional neural network (CNN) object detection model that uses airborne laser scanning data, or lidar, for archaeological feature detection across 615 km2 of the Maya area, as well as preliminary results from an additional 885 km2 test area. This sets the path for a model that will enable researchers to map archaeological areas across the entire Maya Lowland area in weeks or months instead of decades. Notably, we find that a model trained on multiple areas with significantly different topographies produces better results for all areas as compared to a model trained on a single area. The broadscale model here presented produced an F1 score of 0.80. Results also include many potential new structure detections, including detections on lidar at an archaeological area that has not yet been comprehensively ground-surveyed and is located in an entirely different country in the Maya Lowlands from where the model was trained on. This model represents an attempt at a broadscale machine learning approach for archaeological feature mapping in the Maya area and demonstrates how big data can be integrated into traditional archaeological workflows. Lidar has already shown much greater ancient Maya infrastructure throughout the Maya world and elsewhere in the tropics, and this study using machine learning with lidar is showing even greater Maya infrastructure through vast areas of the Maya tropical forest.

Tracking shifts in forest structural complexity through space and time in human‐modified tropical landscapes

Habitat structural complexity is an emergent property of ecosystems that directly shapes their biodiversity, functioning and resilience to disturbance. Yet despite its importance, we continue to lack consensus on how best to define structural complexity, nor do we have a generalised approach to measure habitat complexity across ecosystems. To bridge this gap, here we adapt a geometric framework developed to quantify the surface complexity of coral reefs and apply it to the canopies of tropical rainforests. Using high‐resolution, repeat‐acquisition airborne laser scanning data collected over 450 km2 of human‐modified tropical landscapes in Borneo, we generated 3D canopy height models of forests at varying stages of recovery from logging. We then tested whether the geometric framework of habitat complexity – which characterises 3D surfaces according to their height range, rugosity and fractal dimension – was able to detect how both human and natural disturbances drive variation in canopy structure through space and time across these landscapes. We found that together, these three metrics of surface complexity captured major differences in canopy 3D structure between highly degraded, selectively logged and old‐growth forests. Moreover, the three metrics were able to track distinct temporal patterns of structural recovery following logging and wind disturbance. However, in the process we also uncovered several important conceptual and methodological limitations with the geometric framework of habitat complexity. We found that fractal dimension was highly sensitive to small variations in data inputs and was ecologically counteractive (e.g. higher fractal dimension in oil palm plantations than old‐growth forests), while rugosity and height range were tightly correlated (r = 0.75) due to their strong dependency on maximum tree height. Our results suggest that forest structural complexity cannot be summarised using these three descriptors alone, as they overlook key features of canopy vertical and horizontal structure that arise from the way trees fill 3D space.Keywords: Forest disturbance, LiDAR, logging, recovery, remote sensing, structural complexity

Extraction of Moso Bamboo Parameters Based on the Combination of ALS and TLS Point Cloud Data.

Extracting moso bamboo parameters from single-source point cloud data has limitations. In this article, a new approach for extracting moso bamboo parameters using airborne laser scanning (ALS) and terrestrial laser scanning (TLS) point cloud data is proposed. Using the field-surveyed coordinates of plot corner points and the Iterative Closest Point (ICP) algorithm, the ALS and TLS point clouds were aligned. Considering the difference in point distribution of ALS, TLS, and the merged point cloud, individual bamboo plants were segmented from the ALS point cloud using the point cloud segmentation (PCS) algorithm, and individual bamboo plants were segmented from the TLS and the merged point cloud using the comparative shortest-path (CSP) method. The cylinder fitting method was used to estimate the diameter at breast height (DBH) of the segmented bamboo plants. The accuracy was calculated by comparing the bamboo parameter values extracted by the above methods with reference data in three sample plots. The comparison results showed that by using the merged data, the detection rate of moso bamboo plants could reach up to 97.30%; the R2 of the estimated bamboo height was increased to above 0.96, and the root mean square error (RMSE) decreased from 1.14 m at most to a range of 0.35-0.48 m, while the R2 of the DBH fit was increased to a range of 0.97-0.99, and the RMSE decreased from 0.004 m at most to a range of 0.001-0.003 m. The accuracy of moso bamboo parameter extraction was significantly improved by using the merged point cloud data.

Using the full potential of Airborne Laser Scanning (aerial LiDAR) in wildlife research

AbstractSpecies' habitats are strongly influenced by the 3‐dimensional (3D) structure of ecosystems. The dominant technique used to measure 3D structure is Airborne Laser Scanning (ALS), a type of LiDAR (Light Detection and Ranging) technology. Airborne Laser Scanning captures fine‐scale structural information over large spatial extents and provides useful environmental predictors for habitat modeling. However, due to technical complexities of processing ALS data, the full potential of ALS is not yet realized in wildlife research, with most studies relying on a limited set of 3D predictors, such as vegetation metrics developed principally for forestry applications. Here, we highlight the full potential of ALS data for wildlife research and provide insight into how it can be best used to capture the environmental conditions, resources, and risks that directly determine a species' habitat. We provide a nontechnical overview of ALS data, covering data considerations and the modern options available for creating custom, ecologically relevant, ALS predictors. Options included the following: i) direct point cloud approaches that measure structure using grid, voxel, and point metrics, ii) object‐based approaches that identify user‐defined features in the point cloud, and iii) modeled environmental predictors that use additional modeling to infer a range of habitat characteristics, including the extrapolation of field acquired measurements over ALS data. By using custom ALS predictors that capture species‐specific resources, risks, and environmental conditions, wildlife practitioners can produce models that are tailored to a species' ecology, have greater biological realism, test a wider range of species‐environment relationships across scales, and provide more meaningful insights to inform wildlife conservation and management.

Spatial Prediction of Diameter Distributions for the Alpine Protection Forests in Ebensee, Austria, Using ALS/PLS and Spatial Distributional Regression Models

A novel Bayesian spatial distributional regression model is presented to predict forest structural diversity in terms of the distributions of the stem diameter at breast height (DBH) in the protection forests in Ebensee, Austria. The distributional regression approach overcomes the limitations and uncertainties of traditional regression modeling, in which the conditional mean of the response is regressed against explanatory variables. The distributional regression addresses the complete conditional response distribution, instead. In total 36,338 sample trees were measured via a handheld mobile personal laser scanning system (PLS) on 273 sample plots each having a 20 m radius. Recent airborne laser scanning (ALS) data were used to derive regression covariates from the normalized digital vegetation height model (DVHM) and the digital terrain model (DTM). Candidate models were constructed that differed in their linear predictors of the two gamma distribution parameters. In the distributional regression approach, covariates can enter the model in a flexible form, such as via nonlinear smooth curves, cyclic smooths, or spatial effects. Supported by Bayesian diagnostics DIC and WAIC, nonlinear smoothing splines outperformed linear parametric slope coefficients, and the best implementation of spatial structured effects was achieved by a Gaussian process smooth. Model fitting and posterior parameter inference was achieved by using full Bayesian methodology and MCMC sampling algorithms implemented in the R-package BAMLSS. With BAMLSS, spatial interval predictions of the DBH distribution at any new geo-locations were enabled via straightforward access to the posterior predictive distributions of the model terms and by offering simple plug-in solutions for new covariate values. A cross-validation analysis validated the robustness of the proposed method’s parameter estimation and out-of-sample prediction. Spatial predictions of stem count proportions per DBH classes revealed that regeneration of smaller trees was lacking in certain areas of the protection forest landscape. Therefore, the intensity of final felling needs to be increased to reduce shading from the dense, overmature shelter trees and to promote sunlight for the young regeneration trees.

Estimation of canopy photon recollision probability from airborne laser scanning

The past two decades have witnessed the widespread application of the spectral invariant theory (p-theory) in modeling the shortwave radiation absorbed or scattered by vegetation. The basic principle of this theory is that the canopy reflectance is solely determined by the optical properties of leaves and the photon recollision probability p. As one of the spectral invariants, p serves as a critical bond between the spectral characteristics of the canopy at any wavelength and the reflectance, transmittance, or absorptance of the vegetation canopy, and is intimately associated with the solution of the radiative transfer equation. Currently, estimation of p-value is predominantly accomplished through canopy structural parameters, such as the spherically averaged silhouette to total area ratio (STAR¯) or Leaf Area Index (LAI). In this work, we provide an approach to estimate canopy photon recollision probability directly from Airborne Laser Scanning (ALS) data. We showed that the recollision probability can be interpreted as the interceptions of virtual rays emitted from sampling points within tree crowns described with turbid media, which avoided detailed reconstruction of leaf structures. The approach was evaluated with both virtual experiments as well as field measurements. The virtual experiments employed the Large-scalE remote Sensing data and image Simulation model (LESS) to simulate virtual ALS scanning data based on three RAdiation transfer Model Intercomparison (RAMI) actual canopies, with each divided into 25 segments, for which the p-values could be obtained through LESS. Our findings showed that p-values can be accurately estimated from ALS point clouds. Specifically, it exhibited great consistency with RMSE% less than 5% for broadleaved forest scenes, 25% for mixed forest scenes, and less than 10% for coniferous forest scenes in virtual experiments, respectively, and less than 25% compared to field measurements. This study displays the potential of ALS as a promising tool for forest structure characterization and short-wave radiation modeling in forest ecosystems.

Evaluating the potential for continuous update of enhanced forest inventory attributes using optical satellite data

Abstract Timely and detailed inventories of forest resources are of critical importance to guiding sustainable forest management decisions. As forests occur across large spatial extents, remotely sensed data are often used to augment conventional forest inventory measurements. When combined with field plot measurements, airborne laser scanning (ALS) data can be used to derive detailed enhanced forest inventories (EFIs), which provide spatially explicit and wall-to-wall characterizations of forest attributes. However, these EFIs represent a static point in time, and the dynamic nature of forests, coupled with increasing disturbance and uncertain future conditions, generates a need for the continuous updating of forest inventories. This study used a time series of optical satellite data to update an EFI generated for a large (~690 000 ha) forest management unit in Ontario, Canada, at a two-week interval. The two-phase approach involved first building a relationship between single-year EFI attributes (2018) and spectral variables representing within-year slope, amplitude, and trend of a time series (2000–21) of 14 spectral bands and indices. For each of the 20 strata representing different species groups and site productivity classes, a k-nearest neighbor (kNN) model was developed to impute seven common EFI attributes: aboveground biomass, basal area, stem density, Lorey’s height, quadratic mean diameter, and stem volume. Across all strata, models were generally accurate, with relative root mean square error ranging from 11.47% (canopy cover) to 31.82% (stem volume). In the second phase of the approach, models were applied across the entire study area at two-week intervals in order to assess the capacity of the methodology for characterizing change in EFI attributes over a three-year period. Outputs from this second phase demonstrated the potential of the approach for characterizing changes in EFI values in areas experiencing no change or non-stand replacing disturbances. The methods developed herein can be used for EFI update for any temporal interval, thereby enabling more informed decisions by forest managers to prescribe treatments or understand the current state of forest resources.

Integration of Handheld and Airborne Lidar Data for Dicranopteris Dichotoma Biomass Estimation in a Subtropical Region of Fujian Province, China

Dicranopteris dichotoma is a pioneer herbaceous plant species that is tolerant to barrenness and drought. Mapping its biomass spatial distribution is valuable for understanding its important role in reducing soil erosion and restoring ecosystems. This research selected Luodihe watershed in Changting County, Fujian Province, China, where soil erosion has been a severe problem for a long time, as a case study to explore the method to estimate biomass, including total and aboveground biomass, through the integration of field measurements, handheld laser scanning (HLS), and airborne laser scanning (ALS) data. A stepwise regression model and an allometric equation form model were used to develop biomass estimation models based on Lidar-derived variables at typical areas and at a regional scale. The results indicate that at typical areas, both total and aboveground biomass were best estimated using an allometric equation form model when HLS-derived height and density variables were extracted from a window size of 6 m × 6 m, with the coefficients of determination (R2) of 0.64 and 0.58 and relative root mean square error (rRMSE) of 28.2% and 35.8%, respectively. When connecting HLS-estimated biomass with ALS-derived variables at a regional scale, total and aboveground biomass were effectively predicted with rRMSE values of 17.68% and 17.91%, respectively. The HLS data played an important role in linking field measurements and ALS data. This research provides a valuable method to map Dicranopteris biomass distribution using ALS data when other remotely sensed data cannot effectively estimate the understory vegetation biomass. The estimated biomass spatial pattern will be helpful to understand the role of Dicranopteris in reducing soil erosion and improving the degraded ecosystem.

Mapping fault geomorphology with drone-based lidar

The advent of sub-meter resolution topographic surveying has revolutionized active fault mapping. Light detection and ranging (lidar) collected using crewed airborne laser scanning (ALS) can provide ground coverage of entire fault systems but is expensive, while Structure-from-Motion (SfM) photogrammetry from uncrewed aerial vehicles (UAVs) is popular for mapping smaller sites but cannot image beneath vegetation. Here, we present a new UAV laser scanning (ULS) system which overcomes these limitations to survey fault-related topography cost-effectively, at desirable spatial resolutions, and even beneath dense vegetation. In describing our system, data acquisition and processing workflows, we provide a practical guide for other researchers interested in developing their own ULS capabilities. We showcase ULS data collected over faults from a variety of terrain and vegetation types across the Canadian Cordillera and compare them to conventional ALS and SfM data. Due to the lower, slower UAV flights, ULS offers improved ground return density (~260 points/m2 for the capture of a paleoseismic trenching site and ~10–72 points/m2 for larger, multi-kilometer fault surveys) over conventional ALS (~3–9 points/m2) as well as better vegetation penetration than both ALS and SfM. The resulting ~20–50 cm-resolution ULS terrain models reveal fine-scale tectonic landforms that would otherwise be challenging to image.

Validation of beyond visual-line-of-sight drone photogrammetry for terrain and canopy height applications

Aerial drones enable detailed three-dimensional terrain and vegetation canopy reconstructions supporting a diverse set of environmental monitoring applications. Small drone-derived photogrammetric datasets (<5 km2) often extend field-scale observations and enable calibration and validation for satellite-based mapping. Applications usually require data over much larger areas or in remote areas such as the Arctic, yet large-area drone datasets are rare due to aviation regulations usually stipulating that missions adhere to within-visual-line-of-sight (VLOS) conditions. Terrain and vegetation canopy reconstruction based on longer-range drone missions requires evaluation to justify future beyond-visual-line-of-sight (BVLOS) data initiatives. This work analyzed BVLOS drone photogrammetric datasets for terrain and vegetation height applications along a forest-tundra ecotone (FTE) spanning 396 km in northern Canada. Specifically, we: 1) performed validation assessments of derived terrain models and canopy height models, and 2) investigated remote sensing applications supported by BVLOS data in a FTE experiencing the effects of climate change. The BVLOS data covered in excess of 550 km2, increasing conventional drone data coverage by two orders of magnitude. Elevation validation indicated that strong agreement with GNSS benchmarks, Airborne Laser Scanning (ALS) and VLOS drone data (0.15–0.19 m RMSE) is possible, and that vertical RMSEs <2x the image spatial resolution is achievable. To validate vegetation canopy height modelling capabilities, comparisons with NASA's Land, Vegetation, and Ice Sensor (LVIS) full-waveform ALS observations indicated satisfactory agreement for height metrics (R2 = 0.7 to 0.8, RMSEs <1.0 m). Canopies were reconstructed at fine spatial scales, which in turn supported a unique comparison of vegetation height distributions between six land-cover classes and ecoregions along the FTE, and identified discrepancies in how national and international canopy height models represented the FTE. Detected terrain subsidence, including retrogressive thaw slumping and ice-wedge degradation, illustrated how climate-driven permafrost thaw is modifying landscapes and placing infrastructure at increased risk, which requires intensive monitoring. These applications demonstrated how BVLOS-derived data enables broader scale assessments of regional vegetation structure and terrain changes across larger spatial extents than previously possible with VLOS drones. Mapping opportunities will arise in the environmental remote sensing discipline as drone systems are increasingly operated beyond visual range.

Unraveling the characteristic spatial scale of habitat selection for forest grouse species in the boreal landscape

The characteristic spatial scale at which species respond strongest to forest structure is unclear and species-specific and depends on the degree of landscape heterogeneity. Research often analyzes a pre-defined spatial scale when constructing species distribution models relating forest variables with occupancy patterns. This is a limitation, as forest characteristics shape the species use of habitat at multiple spatial scales. To explore the drivers of this relationship, we conducted an in-depth investigation into how scaling forest variables at biologically relevant spatial scales affects occupancy of grouse species in boreal forest. We used 4,790 grouse observations (broods and adults) collected over 39,303 stands for 15 years of four forest grouse species (capercaillie, black grouse, hazel grouse, and willow grouse) obtained from comprehensive Finnish wildlife triangle census data and forest variables obtained from Airborne Laser Scanning and satellite data originally sampled at 16 m resolution. We fitted Generalized Additive Mixed Models linking grouse presence/absence in the Finnish boreal forest with forest stand structure and composition. We estimated the effects of predictor variables aggregated at three spatial scales reflecting the species use of the landscape: local level at stand scale, home range level at 1 km radius, and regional level at 5 km radius. Multi-grain models considering forest-species relationships at multiple scales were used to evaluate whether there is a specific scale at which forest characteristics best predict local grouse occupancy. We found that that the spatial scale affected the predictive capacity of the grouse occupancy models and the characteristic scale of habitat selection was the same (i.e., stand scale) among species. Different grouse species exhibited varying optimal spatial scales for occupancy prediction. Forest structure was more important than compositional diversity in predicting grouse occupancy irrespective of the scale. A limited number of forest predictors related to availability of multi-layered vegetation and of suitable thickets explained the occupancy patterns for all the grouse species at different scales. In conclusion, modeling grouse occupancy using forest predictors at different spatial scales can inform forest managers about the scale at which the species perceive the landscape. This evidence calls for an integrated multiscale approach to habitat modelling for forest species.

Risk of Tree Fall on High-Traffic Roads: A Case Study of the S6 in Poland

Modern technologies, such as airborne laser scanning (ALS) and advanced data analysis algorithms, allow for the efficient and safe use of resources to protect infrastructure from potential threats. This publication presents a study to identify trees that may fall on highways. The study used free measurement data from airborne laser scanning and wind speed and direction data from the Institute of Meteorology and Water Management in Poland. Two methods were used to determine the crown tops of trees: PyCrown and OPALS. The effect of wind direction on potential hazards was then analyzed. The OPALS method showed the best performance in terms of detecting trees, with an accuracy of 74%. The analysis showed that the most common winds clustered between 260° and 290°. Potential threats, i.e., trees that could fall on the road, were selected. As a result of the analysis, OPALS detected between 140 and 577 trees, depending on the chosen strategy. The presented research shows that combining ALS technology with advanced algorithms and wind data can be an effective tool for identifying potential hazards associated with falling trees on highways.

Long-term monitoring of desert restoration processes using historical stereoscopic imagery and laser scanning data: An example of the Błędów Desert (Poland)

This study aimed to present the spatiotemporal changes associated with the loss and subsequent regaining of the original character of the Błędów Desert (Poland) as a result of restoration efforts. The study area is one of the few places in Europe with a sandy desert character. The research period covered nearly 40 years, covering an area of approximately 1400 ha.The methodology is based on comparing the height structure parameters by analyzing point clouds created from contemporary and historical stereoscopic images. For this purpose, four sets of stereoscopic images were used, including two historical analog images from 1982 to 1996 and contemporary digital data from 2012 to 2018. Additional datasets such as airborne laser scanning data and cadastral databases were used as auxiliary data. The results showed an increase in the area covered by tall vegetation from 2% in 1982 to 18% and 41% in 1996 and 2012, respectively, which decreased to 30% after the completion of restoration activities in 2018. The restoration process has enabled the revival of the original desert character of the study area, although the total area covered by sand decreased compared to its state in the early 1980s. These results indicate the significant potential of the presented methodology in supporting ecosystem management systems, where changes in the height structure of vegetation play a crucial role. This applies to monitoring land abandonment, forest management, and large-scale reclamation work.

Robust retrieval of forest canopy structural attributes using multi‐platform airborne LiDAR

AbstractLiDAR data acquired from airplanes and helicopters – known as airborne laser scanning (ALS) – are widely regarded as the gold standard for characterizing the 3D structure of forests at scale. But in the last decade, advances in unoccupied aerial vehicle (UAV) technologies have led to a rapid rise in the use of UAV laser scanning (ULS) for mapping forest structure. As both ALS and ULS data become increasingly available, they are being used to derive an ever‐growing number of metrics designed to measure different facets of canopy structure. However, which metrics can be robustly retrieved from both ALS and ULS platforms remains unclear. To address this question, we acquired coincident, high‐density ALS and ULS scans covering 115 plots (4‐ha in size) in an open‐canopy temperate ecosystem in Western Australia. Using this unique dataset, we quantified 32 canopy structural metrics related to canopy height, openness and heterogeneity, including metrics calculated directly from the point clouds and ones measured from derived canopy height models (CHM). Overall, we found that ALS and ULS‐derived metrics were strongly correlated (r2 = 0.90). However, this high degree of correlation masked considerable systematic differences between platforms. Specifically, point cloud metrics were less strongly (r2 = 0.87) correlated and had higher bias (10.7%) compared to CHM‐derived ones (r2 = 0.98; bias = 2.5%). Similarly, metrics of canopy openness and heterogeneity were less strongly correlated (r2 = 0.84 and 0.87) and exhibited greater bias (14.4 and 7.9%) than ones relating to canopy height (r2 = 0.96; bias = 3.8%). Our results indicate that only a small subset of the 32 metrics we tested were directly comparable between ALS and ULS platforms. Consequently, future efforts to combine laser scanning data across platforms and instruments should think carefully about which metrics are most appropriate, especially when working with point cloud data.

Elevation Accuracy of Forest Road Maps Derived from Aerial Imaging, Airborne Laser Scanning and Mobile Laser Scanning Data

Forest road maps are a fundamental source of information for the sustainable management, protection, and public utilization of forests. However, the precision of these maps is crucial to their use. In this context, we assessed and compared the elevation accuracy of terrain on three forest road surfaces (i.e., asphalt, concrete, and stone), which were derived based on data from three remote sensing technologies (i.e., aerial imaging, airborne laser scanning, and mobile laser scanning) using five geospatial techniques (i.e., inverse distance; natural neighbor; and conversion by average, maximal, and minimal elevation value). Specifically, the elevation accuracy was assessed based on 700 points at which elevation was measured in the field, and these elevations were extracted from fifteen derived forest road maps with a resolution of 0.5 m. The highest precision was found on asphalt roads derived from mobile laser scanning data (RMSE from ±0.01 m to ±0.04 m) and airborne laser scanning data (RMSE from ±0.03 m to ±0.04 m). On the other hand, the lowest precision was found on all roads derived from aerial imaging data (RMSE from ±0.11 m to ±0.23 m). Furthermore, we found significant differences in elevation between the measured and derived terrains. However, the differences in elevation between specific techniques, such as inverse distance, natural neighbor, and conversion by average, were mostly random. Moreover, we found that airborne and mobile laser scanning technologies provided terrain on concrete and stone roads with random elevation differences. In these cases, it is possible to replace a specific technique or technology with one that is similar without significantly decreasing the elevation accuracy (α = 0.05).

Integration of Airborne Laser Scanning data into forest ecosystem management in Canada: Current status and future directions

Airborne Laser Scanning (ALS) has been the subject of decades of applied research and development in forest management. ALS data are spatially explicit, capable of accurately characterizing vegetation structure and underlying terrain, and can be used to produce value-added products for terrestrial carbon assessments, hydrology, and biodiversity among others. Scientific support for ALS is robust, however its adoption within environmental decision-making frameworks remains inconsistent. Cost continues to be a principal barrier limiting adoption, especially in remote, forested regions, however added challenges such as the need for technical expertise, unfamiliarity of data capabilities and limitations, data management requirements, and processing logistics also contribute. This review examines the current status of the integration of ALS data into forest ecosystem management in a Canadian context. We advocate for continued inter-agency acquisitions leading to integration of ALS into existing natural resource management decision pathways. We gauge the level of uptake thus far, discuss the barriers to operational implementation at provincial scales, and highlight how we believe ALS can support multiple objectives of forest and environmental management in Canada. We speak to potential benefits for supporting inter-agency terrain generation, ecosystem mapping, biodiversity assessments, silvicultural planning, carbon and forest health evaluations, and riparian characterizations. We conclude by providing key considerations for developing capacity using ALS and discuss the technologies future in the context of Canadian forest and environmental management objectives.

Tree growth potential and its relationship with soil moisture conditions across a heterogeneous boreal forest landscape

Forest growth varies across landscapes due to the intricate relationships between various environmental drivers and forest management. In this study, we analysed the variation of tree growth potential across a landscape scale and its relation to soil moisture. We hypothesised that soil moisture conditions drive landscape-level variation in site quality and that intermediate soil moisture conditions demonstrate the highest potential forest production. We used an age-independent difference model to estimate site quality in terms of maximum achievable tree height by measuring the relative change in Lorey’s mean height for a five year period across 337 plots within a 68 km2 boreal landscape. We achieved wall-to-wall estimates of site quality by extrapolating the modelled relationship using repeated airborne laser scanning data collected in connection to the field surveys. We found a clear decrease in site quality under the highest soil moisture conditions. However, intermediate soil moisture conditions did not demonstrate clear site quality differences; this is most likely a result of the nature of the modelled soil moisture conditions and limitations connected to the site quality estimation. There was considerable unexplained variation in the modelled site quality both on the plot and landscape levels. We successfully demonstrated that there is a significant relationship between soil moisture conditions and site quality despite limitations associated with a short study period in a low productive region and the precision of airborne laser scanning measurements of mean height.

Spatio-Temporal Morphodynamics of a Nourished Sandy Shore Based on LiDAR Measurements

Coastal erosion is a pervasive global phenomenon, exemplified by the Hel Peninsula situated in the Gulf of Gdańsk, the southern Baltic Sea. The geological constitution of the Hel Peninsula, characterized by sandy and loosely consolidated material, predisposes its coastal zones to continual morphological changes. The peninsula’s limited width and elevation exacerbate shoreline erosion, particularly during periods of heightened storm activity. This study scrutinizes the effectiveness of coastal nourishment interventions, with a specific focus on segments influenced by the Władysławowo port and the Kuźnica vicinity, over several years. This specific section of the coast serves as a significant case study due to its role as a transit zone for sand transport along the whole peninsula. Protective measures, including shore nourishments and coastal groynes, aim to mitigate erosion impacts. Utilizing Airborne Laser Scanning (ALS) data spanning from 2008 to 2022, erosion dynamics were analyzed. The analysis reveals significant erosion patterns coinciding with the frequency and volume of nourishment material deposition, particularly evident in heavily nourished areas proximate to Władysławowo and Kuźnica. Despite persistent monitoring endeavors, persistent erosive trends pose imminent threats to Kuźnica’s infrastructure, necessitating further research into the efficacy of implemented coastal protection measures.