Search Results (235)

Integrating existing tree-level information into harvester operator decision-making can significantly enhance precision forest management, particularly with respect to biodiversity preservation and climate-smart adaptation. During harvester operations, a primary challenge lies in positioning the machine with sufficient accuracy in real time to relate a priori individual-tree-level reference information to the operator. We propose a lightweight procedure using tree-to-tree matching to continuously register a real-time tree map collected from a harvester (or another mobile laser scanning system) to a precomputed reference map derived from an airborne laser scanner (ALS). We assess the robustness of the method using simulated tree maps and validate its real-world performance in experiments using a LiDAR-equipped harvester performing a thinning operation in a boreal forest. In simulations, registration was found to be robust up to a moderate tree density of approximately 1700 ha⁻¹, even when using a reference map with a lower positional accuracy and higher error rates than in our harvester experiments. Using real-world data from the thinning operation, the registration method was demonstrated to successfully mitigate meter-scale positioning drifts remaining in the LiDAR-inertial trajectory. After the continuous registration procedure, the positioning error was reduced to the level of 0.5 m, constrained by the accuracy of the prior map derived from sparse ALS data with ∼5 transmissions/m². Importantly, the registration procedure was shown to update in real time (at most 20 ms update time for stands with densities of at most 2000 ha⁻¹, after an initial computational phase. Notable features of the registration procedure are its low memory consumption, fast runtime and capacity to accurately position the harvester despite LiDAR-inertial positioning drift. While these results demonstrate the potential for real-time operation, full implementation requires the development of real-time tree detection and estimation of tree attributes. Full article

Artificial intelligence (AI), cloud computing, robotics, automation, and remote sensing technologies are all contributing to digital transformation in forestry. Improving on low-accuracy Global Navigation Satellite Systems (GNSS) positioning affected by multipath error and interception under forest canopies is critical for integrating smart and digital technologies into equipment in forest operations. In an era where lidar-derived individual tree locations are now increasingly available in digital forest inventories, a possible alternative approach to positioning resources such as people or equipment accurately could be to match locally-measured tree positions and attributes in the forest with an existing global reference map based on prior remote sensing missions, effectively using the trees themselves as satellites to circumvent the need for GNSS-based positioning. We evaluated a lidar-based alternative to GNSS positioning using predicted tree positions from local terrestrial laser scanning (TLS) matched with a global stem map derived from prior airborne laser scanning (ALS), a methodology we refer to as TreePS. The horizontal error of the TreePS system was estimated using 154 permanent single-tree inventory plots on the University of Idaho Experimental Forest with two different workflows based on two common R packages (lidR v. 4.3.0, FORTLS v. 1.6.2) using either spatial coordinates or spatial plus stem DBH predicted using one or both segmentation routines and a custom matching algorithm. Mean TreePS error using lidR for below and above-canopy segmentation had mean error of 1.04 and 2.04 m with 93.5% and 91.6% of plots with viable match solutions on spatial and spatial plus DBH matching. The second workflow with both FORTLS (TLS point cloud) and lidR (ALS point cloud) had errors of 1.09 and 2.67 m but only 57.9% and 54.2% of plots with solutions using spatial and spatial plus DBH, respectively. There is room for improvement in the matching algorithm but the TreePS methodology and similar feature-matching solutions may be useful for below-canopy positioning of equipment, people or other resources under dense forests and other GNSS-degraded environments to help advance smart and digital forestry. Full article

We report the design, epitaxial growth, and room-temperature operation of a high-gain AlInAsSb-based avalanche photodiode (APD) for short-wavelength infrared (SWIR) detection at 1.55 µm. The device employs SAGCM structure to confine the electric field within the multiplication region while suppressing dark current. High-quality AlInAsSb layers were grown on GaSb substrates by molecular beam epitaxy using a digital alloy approach, achieving excellent surface morphology (R_a < 0.2 nm) and uniform superlattice periodicity. Electrical characterization reveals a well-defined breakdown voltage near −17 V and a peak internal multiplication gain of 200 at 300 K under 0.2 mW illumination at 1550 nm—among the highest gains reported to date for antimonide-based APDs operating at room temperature. Variable-temperature dark current analysis indicates a transition from tunneling-dominated to thermally generated dark current as temperature increases from 100 K to 300 K. These results demonstrate the strong potential of AlInAsSb SAGCM APDs for eye-safe, high-sensitivity applications in LIDAR, free-space optical communication, and low-light SWIR imaging. Full article

Airborne laser scanning (ALS) and UAV-mounted LiDAR sensors have become well-established tools for identifying and mapping archaeological features across varying scales and contexts. Numerous algorithms have been developed over the years for generating Digital Terrain or Features Models (DTMs/DFMs), which provide an accurate representation of the ground or structures’ surface, serving as the foundation for subsequent archaeological analyses. In this study, we report the developed multi-level multi-resolution (MLMR) methodology, based on machine/deep learning methods, for DFM generation through point cloud semantic segmentation. The work also compares different approaches and the impact of the resolution on their performance. To this end, each approach’s performance is evaluated with a series of quantitative and qualitative analyses, with an eye on hardware limitations and time constraints. Three test sites from Mediterranean and Alpine environments, with manually annotated ground truth data, are used for the evaluation of each methodological approach. Full article

Sustainable forest management requires current, territory-wide data, which is difficult to obtain in vast regions like Quebec, Canada. To complement ground inventories and photo-interpretation, the province developed an airborne laser scanning (ALS)-based model that performs well in coniferous stands, but its accuracy in hardwood stands remains untested. This study aims to evaluate the accuracy of the ALS-based prediction of stand basal area and then test new approaches to increase its performance. Airborne LiDAR data from 2011 to 2020 and 12,506 validation plots from sample plots were used. The ALS model accuracy was initially compared across the stand types, revealing lower accuracy in shade-tolerant deciduous stands. Three inputs were found to increase prediction accuracy: proportion of each species basal area in the stand, geographical coordinates, and meteorological data associated with location. Parametric and auto machine learning (AutoML) methods were employed using those inputs to improve accuracy, with AutoML achieving the highest improvement with initial R² of 0.27, 0.47 and 0.54 and after correction R² of 0.31, 0.56 and 0.67, respectively, for shade-tolerant deciduous, shade-intolerant deciduous, and coniferous stand. Even with the advancements made, further improvements will be necessary to consider using an ALS-based model for shade-tolerant deciduous species. Full article

Field data of high accuracy and precision is the basis for creating the high-quality design of a forest road. In this study, three survey methods for collecting field data were tested: ALS UAV, LiDAR data of the Republic of Croatia, collected by airplane, and UAV SfM. A total of three detailed forest road projects were created based on the collected data. The designed forest roads had the same horizontal and vertical development, thus eliminating the human factor from the design process. Four important forest road parameters were tested: earthwork cut and fill volume, cross-terrain slope, and carriageway value. No significant statistical difference was found for any of the tested parameters between designs. The design based on ALS data had a total number of earthworks of 1026.03 m³, the amount was 1449.56 m³ for SfM design, and the number of earthworks for the State Geodetic Administration LiDAR data was 889.02 m³. The calculated amount of cut volume was significantly affected by the error of the carriageway value for the State Geodetic Administration LiDAR data-based design. The results indicate the possibility of using all used methods on terrain with a moderate slope, but there is a need for further testing on different terrain slope classes. Full article

This study evaluated airborne laser scanning (ALS) as a large-scale tool for forest carbon quantification by comparing ALS-derived estimates with traditional field sampling across multiple forest strata. Above-ground biomass was estimated using two different, commonly used equations, while below-ground biomass was derived from peer-reviewed root-to-shoot ratios. ALS and field estimates differed across forest strata and carbon pools: ALS detected higher live tree carbon in harvested areas—capturing residual trees often missed in traditional cruises—but underestimated dead wood carbon, relative to field-based methods. Consistent differences were also observed between biomass equations, with Woodall estimates being 12.8% and 16.7% lower than Jenkins estimates for ALS and field methods, respectively. The study further incorporated soil organic carbon (SOC) and carbon dating data, providing additional insight into subsurface carbon stocks and the temporal dynamics of forest carbon pools. Overall, ALS proved to be an efficient, repeatable, and scalable method for carbon assessment, offering clear advantages in monitoring carbon flux over time when integrated with forest management protocols. Although further research is needed to refine biomass equations and explore emerging technologies such as Geiger Mode LiDAR, ALS has strong potential to enhance forest carbon crediting processes and support climate change mitigation goals. Full article

Accurate Digital Terrain Models (DTMs) are essential for managing forest drainage networks as a crucial element of water management, yet dense canopies and complex micro-topography challenge Airborne Laser Scanning (ALS) precision. This study evaluates the vertical accuracy of an ALS-derived DTM specifically within forest drainage ditches, utilizing 706 GNSS and total station measurements for validation. The results indicate a positive elevation bias, with a mean elevation error of 0.415 m and an RMSE of 0.464 m, 54.7% higher than the 0.3 m declared in the DTM technical report. Forest height, acting as a proxy for forest structural density and canopy closure, was significantly associated with a reduction in ground reflection density and an increase in the distance to the nearest ground reflection (p < 0.05). Mixed-effects ANOVA confirmed that there are significantly more ground reflections in low vegetation (0–1 m). Crucially, multiple regression analysis revealed that forest height was not the primary driver of elevation error; instead, ditch geometry was the most significant predictor. Narrower ditches exhibited substantially higher errors than wider ones, regardless of the canopy height. Furthermore, while ground reflection density decreased in mature stands, this reduction did not significantly diminish DTM vertical accuracy, suggesting that some of the LiDAR reflections of low vegetation could be misclassified as ground reflections, decreasing accuracy. These findings suggest that while ALS is effective for general forest topography and mapping drainage infrastructure, its application may require corrections for ditch dimensions rather than vegetation height alone to mitigate systematic overestimation of ditch bed elevations. Full article

Near-infrared (NIR) photodetectors are essential for LiDAR, optical communication, and sensing technologies requiring fast response and low power consumption. This work reports a PN photodiode incorporating a co-sputtered MoS₂–Al₂O₃ composite layer to enhance NIR photoresponse for LiDAR and environmental sensing applications. The composite layer improves device performance through defect passivation, dielectric screening, and modified carrier transport behavior. Under 100 mW·cm⁻² illumination at 4 V, the device delivers a photocurrent of 10 mA with a response time of 155 µs, corresponding to an approximately threefold (~300%) improvement compared to a reference structure. Spectral measurements show peak responsivity at 970 nm with extended sensitivity up to 1100 nm. These results indicate that embedding Al₂O₃ within the MoS₂ improves the MoS₂/Si interface and facilitates infrared photon absorption in the Si substrate, leading to enhanced vertical carrier collection and reduced recombination compared with conventional surface-passivated MoS₂/dielectric layers-based devices. The proposed device demonstrates a low-cost, broadband photodiode architecture suitable for eye-safe LiDAR and environmental monitoring applications. Full article

Over the past two decades, Light Detection and Ranging (LiDAR) technology has evolved from early National Aeronautics and Space Administration (NASA)-led airborne laser altimetry into commercially mature systems that now underpin vegetation remote sensing across scales. Continuous advancements in laser engineering, signal processing, and complementary technologies—such as Inertial Measurement Units (IMU) and Global Navigation Satellite Systems (GNSS)—have yielded compact, cost-effective, and highly sophisticated LiDAR sensors. Concurrently, innovations in carrier platforms, including uncrewed aerial systems (UAS), mobile laser scanning (MLS), Simultaneous Localization and Mapping (SLAM) frameworks, have expanded LiDAR’s observational capacity from plot- to global-scale applications in forestry, precision agriculture, ecological monitoring, Above Ground Biomass (AGB) modeling, and wildfire science. This review synthesizes LiDAR’s cross-domain capabilities for the following: (a) quantifying vegetation structure, function, and compositional dynamics; (b) recent sensor developments encompassing ALS discrete-return ${(ALS}_D)$, and ALS full-waveform $\left({ALS}_{FW}\right)$, photon-counting LiDAR (PCL), emerging multispectral LiDAR (MSL), and hyperspectral LiDAR (HSL) systems; and (c) state-of-the-art data processing and fusion workflows integrating optical and radar datasets. The synthesis demonstrates that many LiDAR-derived vegetation metrics are inherently transferable across domains when interpreted within a unified structural framework. The review further highlights the growing role of artificial-intelligence (AI)-driven approaches for segmentation, classification, and multitemporal analysis, enabling scalable assessments of vegetation dynamics at unprecedented spatial and temporal extents. By consolidating historical developments, current methodological advances, and emerging research directions, this review establishes a comprehensive state-of-the-art perspective on LiDAR’s transformative role and future potential in monitoring and modeling Earth’s vegetated ecosystems. Full article

NASA’s ICESat-2 mission offers potential for coastal monitoring by combining its land/vegetation (ATL08) and nearshore bathymetry (ATL24) products. However, the combined performance of these products in environments where both canopy and bathymetry are present, such as mangroves, has not been explored. This work assesses ATL08 and ATL24 over mangroves using a dual approach: (1) a detailed regional validation in Everglades National Park against high-resolution airborne lidar (ALS), and (2) a global analysis characterizing mangrove structure. The regional validation found strong accuracies, with a root mean square error (RMSE) of 1.63 m for ATL08 canopy height and 0.25 m for ATL24 bathymetry for 10 m segments. In this comparison, using 30 m segments, ICESat-2 (RMSE 1.37 m) demonstrated superior performance to GEDI (RMSE 1.51 m) when measuring the same mangrove canopies. The global analysis confirmed that the majority of mangroves are short-stature (<10 m), a structural range where ICESat-2 demonstrates optimal performance. Despite these strengths, disagreements in photon labels between the ATL08 and ATL24 algorithms limit the ability to identify differences between topography, bathymetry, and water surface in these intertidal areas. While ICESat-2 has potential to accurately measure canopy height and bathymetry in mangroves, the integrated mapping beneath dense canopies is not yet feasible with standard products. Full article

Three-dimensional (3D) building models are essential for urban planning, spatial analysis, and virtual simulations. However, most reconstruction methods based on Airborne LiDAR Scanning (ALS) rely primarily on rooftop information, often resulting in distorted footprints and the omission of façade semantics such as windows and doors. To address these limitations, this study proposes an automatic 3D building reconstruction method driven by façade geometry. The proposed method introduces three key contributions: (1) a façade-guided footprint generation strategy that eliminates geometric distortions associated with roof projection methods; (2) robust detection and reconstruction of façade openings, enabling reliable identification of windows and doors even under sparse ALS conditions; and (3) an integrated volumetric modeling pipeline that produces watertight models with embedded façade details, ensuring both structural accuracy and semantic completeness. Experimental results show that the proposed method achieves geometric deviations at the decimeter level and feature recognition accuracy exceeding 97%. On average, the reconstruction time of a single building is 91 s, demonstrating reliable reconstruction accuracy and satisfactory computational performance. These findings highlight the potential of the method as a robust and scalable solution for large-scale ALS-based urban modeling, offering substantial improvements in both structural precision and semantic richness compared with conventional roof-based approaches. Full article

This paper presents the design and fabrication of a 1.65 μm tapered semiconductor laser based on an InGaAlAs multiple quantum well structure (grown) on InP. Through theoretical modeling and parametric optimization simulations, it was established that an etching depth of 0.8 μm for the ridge waveguide and a taper angle of 6° effectively confine the optical field and suppress high-order mode lasing. Based on these optimized parameters, a tapered semiconductor laser with a ridge width of 2 μm and a cavity length of 2000 μm was successfully fabricated. Systematic characterization was conducted under continuous-wave operation at 25 °C. The device exhibits outstanding overall performance: a maximum continuous-wave output power of 19.3 mW, a peak wavelength of 1653 nm, a spectral line width of 0.793 nm, and a side-mode suppression ratio (SMSR) as high as 49 dB, demonstrating excellent spectral purity. Far-field measurements further reveal that at an injection current of 30 mA, the vertical and horizontal far-field divergence angles are 41.02° and 15.26°, respectively, with a well-defined Gaussian beam profile. This study provides an effective technical approach for the design and fabrication of high-performance semiconductor lasers in the 1.65 μm band. The developed device shows significant potential for applications in free-space optical communication, LiDAR, and gas sensing. Full article

In recent years, remote sensing technologies have become indispensable for the documentation, analysis, and virtual preservation of historical, architectural, and archaeological heritage. Advances in 3D scanning have enabled the precise digital recording of complex structures as large-scale point clouds, facilitating highly detailed virtual reconstructions. This study evaluates the capability of LiDAR-based Terrestrial Laser Scanning (TLS) for documenting historical monument façades within a 3D environment and generating accurate visualisation models from registered, colourised point clouds. The integration of high-resolution RGB imagery, processed through Reality Capture 1.5 software, enables the automatic production of realistic 3D models that combine geometric accuracy with visual fidelity. Simultaneously, Geographic Information Systems (GIS), particularly cloud-based platforms like ArcGIS Pro Online, enhance spatial data management, mapping, and analysis. When combined with TLS, GIS is part of a broader remote sensing framework that improves heritage documentation regarding precision, speed, and interpretability. The digital survey of the Shanasheel house in Al-Basrah, Iraq, demonstrates the effectiveness of this interdisciplinary approach. These architecturally and culturally significant buildings, renowned for their intricately decorated wooden façades, were digitally recorded using CAD-based methods to support preservation and mitigation against urban and environmental threats. This interdisciplinary workflow demonstrates how remote sensing technologies can play a vital role in heritage conservation, enabling risk assessment, monitoring of urban encroachment, and the protection of endangered cultural landmarks for future generations. Full article

The shoreline and seabed topography are key components of the coastal zone and are essential for hydrographic surveys, shoreline process modelling, and coastal infrastructure management. The development of unmanned aerial vehicles (UAVs) and optoelectronic sensors, such as photogrammetric cameras and airborne laser scanning (ALS) using light detection and ranging (LiDAR) technology, has enabled the acquisition of high-resolution bathymetric data with greater accuracy and efficiency than traditional methods using echo sounders on manned vessels. This article presents a spatial analysis of bathymetric data obtained from UAV photogrammetry and ALS LiDAR, focusing on shallow-water depth estimation and shoreline extraction. The study area is Lake Kłodno, an inland waterbody with moderate ecological status. Aerial imagery from the photogrammetric camera was used to model the lake bottom in shallow areas, while the LiDAR point cloud acquired through ALS was used to determine the shoreline. Spatial analysis of support vector regression (SVR)-based bathymetric data showed effective depth estimation down to 1 m, with a reported standard deviation of 0.11 m and accuracy of 0.22 m at the 95% confidence, as reported in previous studies. However, only 44.5% of 1 × 1 m grid cells met the minimum point density threshold recommended by the National Oceanic and Atmospheric Administration (NOAA) (≥5 pts/m²), while 43.7% contained no data. In contrast, ALS LiDAR provided higher and more consistent shoreline coverage, with an average density of 63.26 pts/m², despite 27.6% of grid cells being empty. The modified shoreline extraction method applied to the ALS data achieved a mean positional accuracy of 1.24 m and 3.36 m at the 95% confidence level. The results show that UAV photogrammetry and ALS laser scanning possess distinct yet complementary strengths, making their combined use beneficial for producing more accurate and reliable maps of shallow waters and shorelines. Full article