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Cotton is one of the crops that requires the most time and labor. Precision agriculture technology is required for efficient management of cotton, and the identification of cotton attribute information in the field is a necessary and crucial step towards implementing precision agriculture. Unmanned aerial vehicles (UAVs) and Light Detection and Ranging (LiDAR) have evolved into essential instruments for plant phenotyping research. In this study, in order to address the demand for cotton attribute identification over wide areas in the field, an airborne LiDAR system was built based on LiDAR detection technology. This work acquired a dual-view point cloud of a cotton field in order to address the high density and low accuracy of the cotton point cloud attributes. Following pre-processing of the data, the point cloud was first coarsely regenerated using a combination of Fast Point Feature Histograms (FPFH) and Intrinsic Shape Signatures (ISS) techniques. The dual-view point cloud registration was then refined and finished using an Iterative Closest Point (ICP) algorithm. The height of the cotton plant was determined using the reconstructed point cloud of the cotton canopy, and a method combining Graham's algorithm and the Alpha-Shape algorithm was suggested to determine the porosity of the cotton layers. The findings revealed that the root mean square errors (RMSE) between calculated and measured values of cotton plant height and stratified porosity were, respectively, 3.98 cm and 5.21%, and that their mean absolute percentage errors (MAPE) were 4.39% and 9.31%, with correlation coefficients (${R^2}$ R 2 ) of 0.951 and 0.762, respectively. On the whole, our study has demonstrated the effectiveness of the proposed method in terms of providing accurate and reliable cotton parameters in agriculture. [ABSTRACT FROM AUTHOR]

Processing measurement data is an essential part of surveying engineering. One can list several methods in such a context: least squares estimation, M-estimation, R-estimation, etc. Some methods were developed by surveyors, e.g., the Danish method, IGG scheme, or Msplit estimation. The last method is, in fact, a class of estimation procedures dedicated to different problems. As a new approach to processing data, Msplit estimation is still being developed and improved. That paper concerns the local robustness of Msplit estimation and introduces a new Msplit estimation variant that is less sensitive to local outliers. Such a property seems important, especially in big data processing, such as observations from Light Detection and Ranging systems. The new variant modifies the squared Msplit estimation (SMS estimation) by implementing the adapted Tukey weight function, hence its acronym SMSTL estimation. The basic theoretical and empirical analyses, which were performed for the univariate model using, among others, the appropriate measures of robustness, confirmed the expected property of the method. The further tests, based on simulated as well as real data, show that the new method might overperform other Msplit estimation variants and classical methods for the chosen types of observation sets. [ABSTRACT FROM AUTHOR]

ABSTRACT The case study area is a small but typical prehistoric landscape in the Kras Plateau on the north coast of the central Mediterranean. The Late Bronze and Iron Age Kras Plateau was an emblematic Mediterranean archaeological landscape dotted with numerous hillforts. Since the mid‐20th century, the landscape had been overgrown with some of the most archaeology‐hostile vegetation, severely impeding landscape archaeology until archaeological LiDAR revealed thousands of archaeological features that attest to a carefully constructed and managed agro‐pastoral landscape. However, these discoveries were hampered by insecure chronology typical of any LiDAR guided analysis. This case study meticulously documented two prehistoric hillforts and a previously unknown agro‐pastoral landscape with hundreds of archaeological features. The focus of the article, however, was on establishing a more precise and objective dating method. We proposed a multiproxy method to date the archaeological landscape. It combines relative dating using remote sensing data and historical maps; dating based on historical context; relative stratigraphic dating; indirect dating based on associated archaeological finds; and dating by association. Particularly, we focused on the association‐based dating of archaeological features. We proposed a method based on the concept of taskscapes that relies on kernel density estimation. Using these methods, we objectively demonstrated that agro‐pastoral landscape features documented with archaeological LiDAR were contemporaneous with Late Bronze Age and Iron Age hillforts and have no connection to the post‐medieval landscape. The latter has important methodological implications for the prehistoric archaeology of Mediterranean karst landscapes, where backdating post‐Medieval landscapes is a common practise. [ABSTRACT FROM AUTHOR]

Airborne laser scanning (ALS) datasets are used widely for estimating forest biometrics. The transferability of predictive models among ALS acquisitions is a topic of research due to differences in timing, flight parameters, equipment specifications, environmental conditions, and processing methods. The transferability of predictive models therefore is subject to uncertainty. This paper presents an evaluation of the transferability of models for the estimation of stem volume and diameter at breast height (DBH) based on individual tree crown size and competitive neighbourhood metrics derived for managed loblolly pine (Pinus taeda) and slash pine (Pinus elliottii) forest in the Southern USA. Two predictive models types were tested: multiple linear regression (MLR) and Rand Forest (RF). We also evaluated the inclusion of additional training data to model development. Models were able to be transferred to other locations with similar structural and management conditions as the original training dataset with little decrease in accuracy, specifically unthinned stands, despite different ALS acquisitions (Plot stem volume: R2 0.7–0.8; NRMSE 10–12%; mean DBH: R2 0.4–0.7; NRMSE 10–17%; plot basal area: R2 0.7–0.8; NRMSE 12%). Increases in structural differences between the training and test data, driven by age or thinning status, introduced unacceptable levels of uncertainty (Stem volume: R2 0.4–0.7; NRMSE 12–16%; mean DBH: R2 0.4–0.5; NRMSE 18–20%; plot basal area: R2 0.5–0.6; NRMSE 22–40%). Generally, RF models most accuracy estimated DBH, and MLR for stem volume. Improvements to estimate accuracy can be achieved through the addition of relatively small datasets, representing features which were not present in the original data. ALS's ability to provide accurate and near-complete inventories of forests hold a great deal of potential for forest management. The existence of a transferable model that can be used across different acquisitions represents a saving in terms of cost and time, we would argue that future research is therefore warranted. Novel LiDAR-based allometric models were tested using new LiDAR acquisitions; LiDAR predictions were based on tree crown size and immediate neighbourhood metrics; Model inputs were derived from drone and crewed aircraft LiDAR acquisitions; Model predictions were most accurate when applied to similar stand structures as the original model; Normalized root means square error for stem volume and diameter in pine stands with no thinning was <15%; Stem size estimates in thinned stands were consistently overestimated. [ABSTRACT FROM AUTHOR]

The Amazon forest contains globally important carbon stocks, but in recent years, atmospheric measurements suggest that it has been releasing more carbon than it has absorbed because of deforestation and forest degradation. Accurately attributing the sources of carbon loss to forest degradation and natural disturbances remains a challenge because of the difficulty of classifying disturbances and simultaneously estimating carbon changes. We used a unique, randomized, repeated, very high-resolution airborne laser scanning survey to provide a direct, detailed, and high-resolution partitioning of aboveground carbon gains and losses in the Brazilian Arc of Deforestation. Our analysis revealed that disturbances directly attributed to human activity impacted 4.2% of the survey area while windthrows and other disturbances affected 2.7% and 14.7%, respectively. Extrapolating the lidar-based statistics to the study area (544,300 km²), we found that 24.1, 24.2, and 14.5 Tg C y-1 were lost through clearing, fires, and logging, respectively. The losses due to large windthrows (21.5 Tg C y-1) and other disturbances (50.3 Tg C y-1) were partially counterbalanced by forest growth (44.1 Tg C y-1). Our high-resolution estimates demonstrated a greater loss of carbon through forest degradation than through deforestation and a net loss of carbon of 90.5 ± 16.6 Tg C y-1 for the study region attributable to both anthropogenic and natural processes. This study highlights the role of forest degradation in the carbon balance for this critical region in the Earth system. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Effective forest management is essential for mitigating climate change effects. This is why understanding forest growth dynamics is critical for its sustainable management. Thus, characterizing forest plot deadwood levels is vital for understanding forest dynamics, and for assessments of biomass, carbon stock, and biodiversity. For the first time, this study used the leaf area index (LAI) and L-moments to characterize and model forest plot deadwood levels in the Bavarian Forest National Park from airborne laser scanning (ALS) data. This study proposes methods that can be tested for forests, especially those in temperate climates with frequent cloud coverage and limited access. The proposed method is practically significant for effective planning and management of forest resources. First, plot decay levels were characterized based on their canopy leaf area density (LAD). Then, the deadwood levels were modeled to assess the relationships between the vegetation area index (VAI), gap fraction (GF), and the third L-moment ratio (T3). Finally, we tested the rule-based methods for classifying plot decay levels based on their biophysical structures. Our results per the LAD vertical profiles clearly showed the declining levels of decay from Level 1 to 5. Our findings from the models indicate that at a 95% confidence interval, 96% of the variation in GF was explained by the VAI with a significant negative association (VAIslope = −0.047; R2 = 0.96; (p < 0.001)), while the VAI explained 92% of the variation in T3 with a significant negative association (VAIslope = −0.50; R2 = 0.92; (p < 0.001)). Testing the rule-based methods, we found that the first rule (Lcv = 0.5) classified Levels 1 and 2 at (Lcv < 0.5) against Levels 3 to 5 at (Lcv > 0.5). However, the second rule (Lskew = 0) classified Level 1 (healthy plots) as closed canopy areas (Lskew < 0) against Levels 2 to 5 (deadwood) as open canopy areas (Lskew > 0). This approach is simple and more convenient for forest managers to exploit for mapping large forest gap areas for planning and managing forest resources for improved and effective forest management. [ABSTRACT FROM AUTHOR]

Imaging spectroscopy is emerging as a leading remote sensing method for quantifying plant biodiversity. The spectral variation hypothesis predicts that variation in plant hyperspectral reflectance is related to variation in taxonomic and functional identity. While most studies report some correlation between spectral and field‐based (i.e., taxonomic and functional) expressions of biodiversity, the observed strength of association is highly variable, and the utility in applying spectral community properties to examine environmental drivers of communities remains unknown. We linked hyperspectral data acquired by airborne imaging spectrometers with precisely geolocated field plots to examine the spectral variation hypothesis along a temperate‐to‐boreal forest gradient in southern Québec, Canada. First, we examine the degree of association between spectral and field‐based dimensions of canopy tree composition and diversity. Second, we ask whether the relationships between field‐based community properties and the environment are reproduced when using spectral community properties. We found support for the spectral variation hypothesis with the strength of association generally greater for the functional than taxonomic dimension, but the strength of relationships was highly variable and dependent on the choice of method or metric used to quantify spectral and field‐based community properties. Using a multivariate approach (comparisons of separate ordinations), spectral composition was moderately well correlated with field‐based composition; however, the degree of association increased when univariately relating the main axes of compositional variation. Spectral diversity was most tightly associated with functional diversity metrics that quantify functional richness and divergence. For predicting canopy tree composition and diversity using environmental variables, the same qualitative conclusions emerge when hyperspectral or field‐based data are used. Spatial patterns of canopy tree community properties were strongly related to the turnover from temperate‐to‐boreal communities, with most variation explained by elevation. Spectral composition and diversity provide a straightforward way to quantify plant biodiversity across large spatial extents without the need for a priori field observations. While commonly framed as a potential tool for biodiversity monitoring, we show that spectral community properties can be applied more widely to assess the environmental drivers of biodiversity, thereby helping to advance our understanding of the drivers of biogeographical patterns of plant communities. [ABSTRACT FROM AUTHOR]

Background: Characterisation of fuel consumption provides critical insights into fire behaviour, effects, and emissions. Stand-replacing prescribed fire experiments in central Utah offered an opportunity to generate consumption estimates in coordination with other research efforts. Aims: We sought to generate fuel consumption maps using pre- and post-fire airborne laser scanning (ALS) and ground measurements and to test the spatial transferability of the ALS-derived fuel models. Methods: Using random forest (RF), we empirically modelled fuel load and estimated consumption from pre- and post-fire differences. We used cross-validation to assess RF model performance and test spatial transferability. Key results: Consumption estimates for overstory fuels were more precise and accurate than for subcanopy fuels. Transferring RF models to provide consumption estimates in areas without ground training data resulted in loss of precision and accuracy. Conclusions: Fuel consumption maps were produced and are available for researchers who collected coincident fire behaviour, effects, and emissions data. The precision and accuracy of these data vary by fuel type. Transferability of the models to novel areas depends on the user's tolerance for error. Implications: This study fills a critical need in the broader set of research efforts linking fire behaviour, effects, and emissions. We used airborne laser scanning (ALS) data and ground measurements to create fuel consumption maps for prescribed fires in central Utah. The data, methods, and accuracy assessments we produced are useful for researchers who collected data on fire behaviour, effects, and emissions on the same fires. [ABSTRACT FROM AUTHOR]

Canopy volume is a crucial biological parameter for assessing tree growth, accurately estimating forest Above-Ground Biomass (AGB), and evaluating ecosystem stability. Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS) are advanced precision mapping technologies that capture highly accurate point clouds for forest digitization studies. Despite advances in calculating canopy volume, challenges remain in accurately extracting the canopy and removing gaps. This study proposes a canopy volume extraction method based on an improved PointNeXt model, fusing ALS and TLS point cloud data. In this work, improved PointNeXt is first utilized to extract the canopy, enhancing extraction accuracy and mitigating under-segmentation and over-segmentation issues. To effectively calculate canopy volume, the canopy is divided into multiple levels, each projected into the xOy plane. Then, an improved Mean Shift algorithm, combined with KdTree, is employed to remove gaps and obtain parts of the real canopy. Subsequently, a convex hull algorithm is utilized to calculate the area of each part, and the sum of the areas of all parts multiplied by their heights yields the canopy volume. The proposed method's performance is tested on a dataset comprising poplar, willow, and cherry trees. As a result, the improved PointNeXt model achieves a mean intersection over union (mIoU) of 98.19% on the test set, outperforming the original PointNeXt by 1%. Regarding canopy volume, the algorithm's Root Mean Square Error (RMSE) is 0.18 m3, and a high correlation is observed between predicted canopy volumes, with an R-Square (R2) value of 0.92. Therefore, the proposed method effectively and efficiently acquires canopy volume, providing a stable and accurate technical reference for forest biomass statistics. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Individual-tree-based models (IBMs) have emerged to provide finer-scale operational simulations of stand dynamics by accommodating and/or representing tree-to-tree interactions and competition. Like stand-level growth model development, IBMs need an array of detailed data from individual trees in any stand through repeated measurement. Conventionally, these data have been collected through forest mensuration by establishing permanent sample plots or temporary measurement plots. With the evolution of remote sensing technology, it is now possible to efficiently collect more detailed information reflecting the heterogeneity of the whole forest stand than before. Among many techniques, airborne laser scanning (ALS) has proved to be reliable and has been reported to have potential to provide unparallel input data for growth models. This study utilized repeated ALS data to develop a model to project the annualized individual tree height increment (ΔHT) in a conifer plantation by considering spatially explicit competition through a mixed-effects modelling approach. The ALS data acquisition showed statistical and biological consistency over time in terms of both response and important explanatory variables, with correlation coefficients ranging from 0.65 to 0.80. The height increment model had high precision (RMSE = 0.92) and minimal bias (0.03), respectively, for model fitting. Overall, the model showed high integrity with the current biological understanding of individual tree growth in a monospecific Pinus radiata plantation. The approach used in this study provided a robust model of annualized individual tree height growth, suggesting such an approach to modelling will be useful for future forest management. [ABSTRACT FROM AUTHOR]

Submerged aquatic vegetation, also referred to as submerged macrophytes, provides important habitats and serves as a significant ecological indicator for assessing the condition of water bodies and for gaining insights into the impacts of climate change. In this study, we introduce a novel approach for the classification of submerged vegetation captured with bathymetric LiDAR (Light Detection And Ranging) as a basis for monitoring their state and change, and we validated the results against established monitoring techniques. Employing full-waveform airborne laser scanning, which is routinely used for topographic mapping and forestry applications on dry land, we extended its application to the detection of underwater vegetation in Lake Constance. The primary focus of this research lies in the automatic classification of bathymetric 3D LiDAR point clouds using a decision-based approach, distinguishing the three vegetation classes, (i) Low Vegetation, (ii) High Vegetation, and (iii) Vegetation Canopy, based on their height and other properties like local point density. The results reveal detailed 3D representations of submerged vegetation, enabling the identification of vegetation structures and the inference of vegetation types with reference to pre-existing knowledge. While the results within the training areas demonstrate high precision and alignment with the comparison data, the findings in independent test areas exhibit certain deficiencies that are likely addressable through corrective measures in the future. [ABSTRACT FROM AUTHOR]

In this work, the use of a low pulse energy and high repetition rate diode pumped solid-state laser was investigated for trigger-free laser induced breakdown spectroscopy (LIBS) based quantitative analysis of commonly used steel grades. Owing to the low pulse energy of 2 mJ, the signal was accumulated over a large number of pulses to obtain a good signal quality and was acquired in a free-running mode, i.e. without any triggering. Although the trigger-free signal acquisition lacked a continuum suppression capability, the weak plasma events caused by the low pulse energy did not produce a significant continuum and hence continuum suppression was not an issue. Further, the 2 kHz repetition rate of the laser required the sample to be continuously scanned to avoid signal decay as a result of the formation of excessively deep craters. As scanning could introduce additional signal fluctuations, a combination of sample scanning velocity and pulse energy leading to the least signal fluctuations was used to acquire LIBS spectra for the steel samples. Of the total 19 samples, 15 steel samples were used to train random forest regression models for the quantitative analysis of various elements, including C, Si, Mn, Cr, Ni, Mo, Cu, and Co. The remaining 4 samples were used for testing/validating the models. It was observed that compared to the full spectral range of ∼185–545 nm, a smaller and information-rich spectral range of ∼185–310 nm produced better results. With the selected spectral range, the relative mean error (RME) ranged from 1.4% to 15.77%, with the lower values belonging to the major elements and the higher values to the minor elements. The root mean square error (RMSE) values were found to range from 0.014 wt% for C to 0.26 wt% for Ni. The R2 values, which indicated the correlation between the predicted and reference concentrations, were found to be >96% for all the elements except Si and Cu. These results suggested that trigger-free LIBS based on a low-pulse-energy, high-repetition-rate laser and a single channel spectrometer covering a narrow spectral range, in combination with machine learning techniques, is suitable for the quantitative analysis of steel elements. [ABSTRACT FROM AUTHOR]

European aspen is a keystone species in boreal forests, which support numerous ecologically important and endangered species. As detection of those species by remote sensing is impossible, we instead investigated the detection of large aspen trees using airborne laser scanning and aerial image data. However, this is a challenge due to their low quantity and scattered occurrence. The performance was assessed with representative and unrepresentative (where aspens were over-represented) samples of the population. First, we detected individual trees and then the random forest (RF) classifier was used to identify large aspens. The RF classification was implemented with and without synthetic minority oversampling technique (SMOTE) to balance the training data due to the rarity of large aspens. At the tree-level, the best F1-score (0.44) was obtained when the unrepresentative plot data were used with SMOTE. However, the F1-score decreased to 0.21 when the representative data were used. The best plot-level (plots with at least one aspen tree) F1-score with the representative plot data was 0.41. We conclude that although data augmentation may improve the result, it is difficult to detect large aspen trees in genuine populations. [ABSTRACT FROM AUTHOR]

This study explores how data from a handheld mobile laser scanning (MLS) system and quantitative structural models (QSM) can be used to estimate tree structural attributes. Four MLS acquisition scenarios were investigated in a 1 ha temperate hardwood stand, including 15 and 35 m parallel lines, nine circular plots, and a 20 m × 20 m grid. Results were compared against terrestrial laser scanning and destructive field measurements. All acquisition scenarios yielded comparable results, except for the 35 m scenario, which showed greater variability. The 20 m × 20 m grid scenario showed the highest accuracy, with an RMSE of 0.41 m (2.07%) for tree height, 3.98 cm (14.93%) for diameter at breast height, 0.21 m³ (19.28%) for merchantable wood volume, and 0.07 m³ (10.11%) for merchantable stem volume. A bias < 5% was observed for these key attributes, except for an 11.68% bias in merchantable wood volume. Overestimation of branch volume was identified as the primary source of bias related to merchantable wood volume. This study highlights MLS's potential for accurate, non-destructive estimation of tree structural attributes, while pointing out the need to refine noise removal and to assess the most suitable acquisition scenarios for various forest types. [ABSTRACT FROM AUTHOR]

Airborne laser scanners produce 3D data that can be used for a range of applications, such as urban planning, facility monitoring, flood mapping, and forest management. Additional information on the surveyed area can be obtained from the backscattered waveforms recorded by modern light detection and ranging (lidar) sensors. However, the high-dimensional representation of full-waveform data has hindered progress in its use due to difficulties in processing and storage. This paper develops a quantized convolutional autoencoder network to compress lidar waveform data into a condensed feature representation, resulting in a compression rate of up to 20:1. This, together with height information, is fed into a U-net convolutional neural network that achieves an accuracy of 93.7% on six classes. [ABSTRACT FROM AUTHOR]

This paper presents a single tree segmentation method applied to 3D point cloud data acquired with a LiDAR scanner mounted on an unmanned aerial vehicle (UAV). The method itself is based on clustering methods and graph theory and uses only the spatial properties of points. Firstly, the point cloud is reduced to clusters with DBSCAN. Those clusters are connected to a 3D graph, and then graph partitioning and further refinements are applied to obtain the final segments. Multiple datasets were acquired for two test sites in the Czech Republic which are covered by commercial forest to evaluate the influence of laser scanning parameters and forest characteristics on segmentation results. The accuracy of segmentation was compared with manual labels collected on top of the orthophoto image and reached between 82 and 93% depending on the test site and laser scanning parameters. Additionally, an area-based approach was employed for validation using field-measured data, where the distribution of tree heights in plots was analyzed. [ABSTRACT FROM AUTHOR]

Airborne laser scanning data are increasingly used to predict forest biomass over large areas. Biomass information cannot be derived directly from airborne laser scanning data; therefore, field measurements of forest plots are required to build regression models. We tested whether simulated laser scanning data of virtual forest plots could be used to train biomass models and thereby reduce the amount of field measurements required. We compared the performance of models that were trained with (i) simulated data only, (ii) a combination of simulated and real data, (iii) real data collected from different study sites, and (iv) real data collected from the same study site the model was applied to. We additionally investigated whether using a subset of the simulated data instead of using all simulated data improved model performance. The best matching subset of the simulated data was sampled by selecting the simulated forest plot with the highest correlation of the return height distribution profile for each real forest plot. For comparison, a randomly selected subset was evaluated. Models were tested on four forest sites located in Poland, the Czech Republic, and Canada. Model performance was assessed by root mean squared error (RMSE), squared Pearson correlation coefficient (r |$^{2}$|⁠), and mean error (ME) of observed and predicted biomass. We found that models trained solely with simulated data did not achieve the accuracy of models trained with real data (RMSE increase of 52–122 %, r |$^{2}$| decrease of 4–18 %). However, model performance improved when only a subset of the simulated data was used (RMSE increase of 21–118 %, r |$^{2}$| decrease of 5–14 % compared to the real data model), albeit differences in model performance when using the best matching subset compared to using a randomly selected subset were small. Using simulated data for model training always resulted in a strong underprediction of biomass. Extending sparse real training datasets with simulated data decreased RMSE and increased r |$^{2}$|⁠ , as long as no more than 12–346 real training samples were available, depending on the study site. For three of the four study sites, models trained with real data collected from other sites outperformed models trained with simulated data and RMSE and r |$^{2}$| were similar to models trained with data from the respective sites. Our results indicate that simulated data cannot yet replace real data but they can be helpful in some sites to extend training datasets when only a limited amount of real data is available. [ABSTRACT FROM AUTHOR]

Radiative transfer models of vegetation play a crucial role in the development of remote sensing methods by providing a theoretical framework to explain how electromagnetic radiation interacts with vegetation in different spectral regions. A limiting factor in model development has been the lack of sufficiently detailed ground reference data on both structural and spectral characteristics of forests needed for testing and validating the models. In this data description paper, we present a dataset on the structural and spectral properties of 58 stands in temperate, hemiboreal and boreal European forests. It is specifically designed for the development and validation of radiative transfer models for forests but can also be utilized in other remote sensing studies. It comprises detailed data on forest structure based on forest inventory measurements, terrestrial and airborne laser scanning, and digital hemispherical photography. Furthermore, the data include spectral properties of the same forests at multiple scales: reflectance spectra of tree leaves and needles (based on laboratory measurements), forest floor (based on in situ measurements) and entire stands (based on airborne measurements), as well as transmittance spectra of tree leaves and needles and entire tree canopies (based on laboratory and in situ measurements, respectively). We anticipate that these data will have wide use in testing and validating radiative transfer models for forests and in the development of remote sensing methods for vegetation. The data can be accessed at: Hovi et al. 2024a, https://doi.org/10.23729/9a8d90cd-73e2-438d-9230-94e10e61adc9 (for laboratory and field data) and Hovi et al. 2024b, https://doi.org/10.23729/c6da63dd-f527-4ec9-8401-57c14f77d19f (for airborne data). [ABSTRACT FROM AUTHOR]

A recently proposed modelling strategy predicts the net primary production (NPP) of forest ecosystems by combining the outputs of a NDVI-driven model, Modified C-Fix, and a bio-geochemical model, BIOME-BGC. This combination strategy takes into account the effects of forest disturbances but still assumes the presence of a mixture of differently aged trees. The application of this strategy to even-aged forests, therefore, requires a methodological advancement aimed at properly modifying the modelling of main ecosystem processes. In particular, the adaptation of the method to even-aged forests is based on the use of high-spatial-resolution airborne laser scanning (ALS) datasets, which yields green and woody biomass estimates that regulate the simulation of photosynthetic and respiratory processes, respectively. This approach was experimented in a Mediterranean study area, San Rossore Regional Park (Central Italy), which is covered by even-aged pine stands in different development phases. The modelling strategy is driven by MODIS NDVI images and meteorological data across five years (2011–2015), which are combined with estimates of forest canopy cover and height obtained from ALS data taken in 2015. This allows the production of stand NPP estimates, which, when converted into respective current annual increment (CAI) values, reasonably reproduce the age dependency of the available ground observations. The CAI estimates also show a highly significant correlation with these observations (r = 0.773) and moderate error levels (RMSE = 2.03 m3 ha−1 year−1, MBE = −0.45 m3 ha−1 year−1). These results confirm the potential of the modified simulation method to yield accurate high-spatial-resolution NPP estimates, which can offer valuable insights into C cycling and storage, in even-aged forests. [ABSTRACT FROM AUTHOR]

Light Detection and Ranging systems serve as robust tools for creating three-dimensional representations of the Earth's surface. These representations are known as point clouds. Point cloud scene segmentation is essential in a range of applications aimed at understanding the environment, such as infrastructure planning and monitoring. However, automating this process can result in notable challenges due to variable point density across scenes, ambiguous object shapes, and substantial class imbalances. Consequently, manual intervention remains prevalent in point classification, allowing researchers to address these complexities. In this work, we study the elements contributing to the automatic semantic segmentation process with deep learning, conducting empirical evaluations on a self-captured dataset by a hybrid airborne laser scanning sensor combined with two nadir cameras in RGB and near-infrared over a 247 km2 terrain characterized by hilly topography, urban areas, and dense forest cover. Our findings emphasize the importance of employing appropriate training and inference strategies to achieve accurate classification of data points across all categories. The proposed methodology not only facilitates the segmentation of varying size point clouds but also yields a significant performance improvement compared to preceding methodologies, achieving a mIoU of 94.24% on our self-captured dataset. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Mapping the terrain and the Earth's surface can be performed through non-contact methoYes, that is correct.ds such as laser scanning. This is one of the most dynamic and effective data collection methods. This case study aims to analyze the usability of spatial data from available sources and to choose the appropriate solutions and procedures for processing the point cloud of the area of interest obtained from available web applications. The processing of the point cloud obtained by airborne laser scanning results in digital terrain models created in selected software. The study also included modeling of different types of residential development, and the results were evaluated. Different data sources may have compatibility issues, which means that the position of the same object from different spatial data databases may not be identical. To address this, deviations of the corresponding points were determined from various data sources such as Real Estate Cadaster, ZBGIS Buildings, LiDAR point cloud, orthophoto mosaic, and geodetic measurements. These deviations were analyzed according to their size and orientation, with the average deviations ranging from 0.22 to 0.34 m and standard deviations from 0.11 to 0.20 m. The Real Estate Cadaster was used as the correct basis for comparison. The area of the building was also compared, with the slightest difference being present between the Real Estate Cadaster and geodetic measurement. The difference was zero after rounding the area to whole numbers. The maximum area difference was +5 m2 for ZBGIS Buildings. [ABSTRACT FROM AUTHOR]

The aim of this review is to assess the impact of cell phone radiation effects on green plants. Rapid progress in networking and communication systems has introduced frequency- and amplitude-modulated technologies to the world with higher allowed bands and greater speed by using high-powered radio generators, which facilitate high definition connectivity, rapid transfer of larger data files, and quick multiple accesses. These cause frequent exposure of cellular radiation to the biological world from a number of sources. Key factors like a range of frequencies, time durations, power densities, and electric fields were found to have differential impacts on the growth and development of green plants. As far as the effects on green plants are concerned in this review, alterations in their morphological characteristics like overall growth, canopy density, and pigmentation to physiological variations like chlorophyll fluorescence and change in membrane potential etc. have been found to be affected by cellular radiation. On the other hand, elevated oxidative status of the cell, macromolecular damage, and lipid peroxidation have been found frequently. On the chromosomal level, micronuclei formation, spindle detachments, and increased mitotic indexes etc. have been noticed. Transcription factors were found to be overexpressed in many cases due to the cellular radiation impact, which shows effects at the molecular level. [ABSTRACT FROM AUTHOR]

Precisely estimating the position, diameter at breast height (DBH), and height of trees is essential in forest resource inventory. Augmented reality (AR)-based devices help overcome the issue of inconsistent global point cloud data under thick forest canopies with insufficient Global Navigation Satellite System (GNSS) coverage. Although monocular simultaneous localization and mapping (SLAM) is one of the current mainstream systems, there is still no monocular SLAM solution for forest resource inventories, particularly for the precise measurement of inclined trees. We developed a forest plot survey system based on monocular SLAM that utilizes array cameras and Inertial Measurement Unit (IMU) sensors provided by smartphones, combined with augmented reality technology, to achieve a real-time estimation of the position, DBH, and height of trees within forest plots. Our results from the tested plots showed that the tree position estimation is unbiased, with an RMSE of 0.12 m and 0.11 m in the x-axis and y-axis directions, respectively; the DBH estimation bias is −0.17 cm (−0.65%), with an RMSE of 0.83 cm (3.59%), while the height estimation bias is −0.1 m (−0.95%), with an RMSE of 0.99 m (5.38%). This study will be useful in designing an algorithm to estimate the DBH and position of inclined trees using point clouds constrained by sectional planes at the breast height of the trunk, developing an algorithm to estimate the height of inclined trees utilizing the relationship between rays and plane positions, and providing observers with visual measurement results using augmented reality technology, allowing them to judge the accuracy of the estimates intuitively. Clearly, this system has significant potential applications in forest resource management and ecological research. [ABSTRACT FROM AUTHOR]

Canopy water content is a fundamental indicator for assessing the level of plant water stress. The correlation between changes in water content and the spectral reflectance of plant leaves at near-infrared (NIR) and short-wave infrared (SWIR) wavelengths forms the foundation for developing a new remote sensing index, the Infrared Canopy Dryness Index (ICDI), to monitor forest dryness that can be used to predict the consequences of water stress. This study introduces the index, that uses spectral reflectance analysis at near-infrared wavelengths, encapsulated by the Normalized Difference Infrared Index (NDII), in conjunction with specific canopy conditions as depicted by the widely recognized Normalized Difference Vegetation Index (NDVI). Development of the ICDI commenced with the construction of an NDII/NDVI feature space, inspired by a conceptual trapezoid model. This feature space was then parameterized, and the spatial region indicative of water stress conditions, referred to as the dry edge, was identified based on the analysis of 10,000 random observations. The ICDI was produced from the combination of the vertical distance (i.e., under consistent NDVI conditions) from an examined observation to the dry edge. Comparisons between data from drought-affected and non-drought-affected control plots in the Australian Northern Jarrah Forest affirmed that the ICDI effectively depicted forest dryness. Moreover, the index was able to detect incipient water stress several months before damage from an extended drought and heatwave. Using freely available satellite data, the index has potential for broad application in monitoring the onset of forest dryness. This will require validation of the ICDI in diverse forest systems to quantify the efficacy of the index. [ABSTRACT FROM AUTHOR]

The management of plantation forests using precision forestry requires advanced inventory methods. Unmanned aerial vehicle laser scanning (ULS) offers a cost-effective approach to accurately estimate forest structural attributes at both plot and individual tree levels. We examined the utility of ULS data collected from a radiata pine stand for tree detection and prediction of diameter at breast height (DBH) and stem volume, using data thinned to 13-point densities (ranging from 10–12,200 points/m2). These datasets were created using a DTM with the highest pulse density and DTMs that used the native decimated point clouds. Models of DBH were constructed using partial least squares (PLS) and random forest (RF) from seven classes of metrics that characterized the horizontal and vertical structure of the canopy. Individual tree segmentation was consistently accurate across the 13-point densities and was insensitive to DTM type (F1 scores > 0.96). Predictions of DBH using PLS models were consistently more accurate than RF models and accuracy was insensitive to the DTM type. Using data from the native DTMs, DBH estimation using PLS had the lowest RMSE of 1.624 cm (R2 of 0.756) at a point density of 12,200 points/m2. Stem volume predictions made using PLS predictions of DBH and height from the ULS had the lowest RMSE of 0.0418 m3 (R2 of 0.792) at 12,200 points/m2. The RMSE values for DBH and volume remained relatively stable from 12,200 to between 750 and 400 points/m2, with reductions in accuracy occurring as point density declined below this threshold. Overall, these findings have significant implications, particularly for the precise estimation of DBH and stem volume at the individual tree level. They demonstrate the potential of cost-effective ULS sensors for rapid and frequent plantation forest assessment, thereby enhancing the application of light detection and ranging (LiDAR) technology in plantation forest management. [ABSTRACT FROM AUTHOR]

Geolocation error in spaceborne sampling light detection and ranging (LiDAR) measurements of forest structure can compromise forest attribute estimates and degrade integration with georeferenced field measurements or other remotely sensed data. Data integration is especially problematic when geolocation error is not well quantified. We propose a general model that uses airborne laser scanning data to quantify and correct geolocation error in spaceborne sampling LiDAR. To illustrate the model, LiDAR data from NASA Goddard's LiDAR Hyperspectral and Thermal Imager (G‐LiHT) was used with a subset of LiDAR data from NASA's Global Ecosystem Dynamics Investigation (GEDI). The model accommodates multiple canopy height metrics derived from a simulated GEDI footprint kernel using spatially coincident G‐LiHT, and incorporates both additive and multiplicative mapping between the canopy height metrics generated from both datasets. A Bayesian implementation provides probabilistic uncertainty quantification in both parameter and geolocation error estimates. Results show a systematic geolocation error of 9.62 m in the southwest direction. In addition, estimated geolocation errors within GEDI footprints were highly variable, with results showing a ∼$$ \sim $$0.45 probability the true footprint center is within 20 m. Estimating and correcting geolocation error via the model outlined here can help inform subsequent efforts to integrate spaceborne LiDAR data, like GEDI, with other georeferenced data. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Accurate estimation of forest terrain and canopy height is crucial for timely understanding of forest growth. Gao Fen-7 (GF-7) Satellite is China's first sub-meter-level three-dimensional (3D) mapping satellite for civilian use, which was equipped with a two-line-array stereo mapping camera and a laser altimeter system that can provide stereo images and full waveform LiDAR data simultaneously. Most of the existing studies have concentrated on evaluating the accuracy of GF-7 for topographic survey in bare land, but few have in-depth studied its ability to measure forest terrain elevation and canopy height. The purpose of this study is to evaluate the potential of GF-7 LiDAR and stereo image for forest terrain and height measurement. The Airborne Laser Scanning (ALS) data were utilized to generate reference terrain and forest vertical information. The validation test was conducted in Pu'er City, Yunnan Province of China, and encouraging results have obtained. The GF-7 LiDAR data obtained the accuracy of forest terrain elevation with RMSE of 8.01 m when 21 available laser footprints were used for results verification; meanwhile, when it was used to calculate the forest height, R2 of 0.84 and RMSE of 3.2 m were obtained although only seven effective footprints were used for result verification. The canopy height values obtained from GF-7 stereo images have also been proven to have high accuracy with the resolution of 20 m × 20 m compared with ALS data (R2 = 0.88, RMSE = 2.98 m). When the results were verified at the forest sub-compartment scale that taking into account the forest types, further higher accuracy (R2 = 0.96, RMSE = 1.23 m) was obtained. These results show that GF-7 has considerable application potential in forest resources monitoring. [ABSTRACT FROM AUTHOR]

GNSS/INS-based positioning must be revised for forest mapping, especially inside the forest. This study deals with the issue of the processability of GNSS/INS-positioned MLS data collected in the forest environment. GNSS time-based point clustering processed the misaligned MLS point clouds collected from skid trails under a forest canopy. The points of a point cloud with two misaligned copies of the forest scene were manually clustered iteratively until two partial point clouds with the single forest scene were generated using a histogram of GNSS time. The histogram's optimal bin width was the maximum bin width used to create the two correct point clouds. The influence of GNSS outage durations, signal strength statistics, and point cloud parameters on the optimal bin width were then analyzed using correlation and regression analyses. The results showed no significant influence of GNSS outage duration or GNSS signal strength from the time range of scanning the two copies of the forest scene on the optimal width. The optimal bin width was strongly related to the point distribution in time, especially by the duration of the scanned plot's occlusion from reviewing when the maximum occlusion period influenced the optimal bin width the most (R2 = 0.913). Thus, occlusion of the sub-plot scanning of tree trunks and the terrain outside it improved the processability of the MLS data. Therefore, higher stem density of a forest stand is an advantage in mapping as it increases the duration of the occlusions for a point cloud after it is spatially tiled. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this work, we evaluated the changes that occurred by the deposition of the tailings, and the consequences after the implementation of rehabilitation procedures, in a stretch of the Gualaxo do Norte River, Brazil. The deposition occurred after the largest environmental disaster in the history of Brazil, the rupture of the Fundão dam, which dumped tons of ore tailings into the environment, producing several losses in the Doce River Basin. We carried out three surveys with laser scanner in the area: the first with airborne laser scanner in 2011, before the dam collapse; and two with terrestrial laser scanner after the disaster, in 2016 and 2018. The cloud points were aligned and evaluated regarding their positional accuracy using Global Navigation Satellite System control points. The changes in sediment volume as well as the material gains or losses among the periods were assessed according to differences in the coordinate Z. The siltation affected both the Gualaxo do Norte channel and the river terraces, with the study area suffering a deposition of almost 40 thousand m3 of sediments. It was also possible to identify zones where the deposition of sediments predominated over removal/erosion, with 68% of the study area in 2016 recording accumulation of tailing layers in relation to the pre-rupture situation. In 2018, the deposition zones corresponded to almost 80% of the study area (in relation to the situation in 2016), and the most material accumulated was associated with topsoil deposition through rehabilitation interventions. Laser scanner technology proved to be promising to estimate the volume of tailings and identify areas with accretion or removal of sediments, being a great tool to direct environmental recovery initiatives in the Fundão dam most affected areas. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Differential penetration of green laser light into snow and ice has long been considered a possible cause of range and thus elevation bias in laser altimeters. Over snow, ice, and water, green photons can penetrate the surface and experience multiple scattering events in the subsurface volume before being scattered back to the surface and subsequently the instrument's detector, therefore biasing the range of the measurement. Newly formed sea ice adjacent to open-water leads provides an opportunity to identify differential penetration without the need for an absolute reference surface or dual-color lidar data. We use co-located, coincident high-resolution natural-color imagery and airborne lidar data to identify surface and ice types and evaluate elevation differences between those surfaces. The lidar data reveals that apparent elevations of thin ice and finger-rafted thin ice can be several tens of centimeters below the water surface of surrounding leads, but not over dry snow. These lower elevations coincide with broadening of the laser pulse, suggesting that subsurface volume scattering is causing the pulse broadening and elevation shift. To complement our analysis of pulse shapes and help interpret the physical mechanism behind the observed elevation biases, we match the waveform shapes with a model of scattering of light in snow and ice that predicts the shape of lidar waveforms reflecting from snow and ice surfaces based on the shape of the transmitted pulse, the surface roughness, and the optical scattering properties of the medium. We parameterize the scattering in our model based on the scattering length Lscat , the mean distance a photon travels between isotropic scattering events. The largest scattering lengths are found for thin ice that exhibits the largest negative elevation biases, where scattering lengths of several centimeters allow photons to build up considerable range biases over multiple scattering events, indicating that biased elevations exist in lower-level Airborne Topographic Mapper (ATM) data products. Preliminary analysis of ICESat-2 ATL10 data shows that a similar relationship between subsurface elevations (restored negative freeboard) and "pulse width" is present in ICESat-2 data over sea ice, suggesting that biased elevations caused by differential penetration likely also exist in lower-level ICESat-2 data products. The spatial correlation of observed differential penetration in ATM data with surface and ice type suggests that elevation biases could also have a seasonal component, increasing the challenge of applying a simple bias correction. [ABSTRACT FROM AUTHOR]

The firn layer on the Greenland Ice Sheet (GrIS) plays a crucial role in buffering surface meltwater runoff, which is constrained by the available firn pore space and impermeable ice layers that limit deeper meltwater percolation. Understanding these firn properties is essential for predicting current and future meltwater runoff and its contribution to global sea-level rise. While very-high-frequency (VHF) radars have been extensively used for surveying the GrIS, their lower bandwidth restricts direct firn stratigraphy extraction. In this study, we use concurrent VHF airborne radar and laser altimetry data collected as part of Operation IceBridge over the 2011–2019 period to investigate our hypothesis that vertical heterogeneities in firn (i.e. ice layers) cause vertical offsets in the radar surface reflection (dz). Our results, corroborated by modelling and firn core analyses, show that a dz larger than 1 m is strongly related to the vertical heterogeneity of a firn profile and effectively delineates between vertically homogeneous and vertically heterogeneous firn profiles over a depth range of ∼ 4 m. Temporal variations in dz align with climatic events and reveal an expansion of heterogeneous firn between 2011–2013 covering an area of ∼ 350 815 km 2 , followed by firn replenishment over the years 2014–2019 spanning an area of ∼ 667 725 km 2. Our approach reveals the firn evolution of key regions on the Greenland Ice Sheet, providing valuable insights for detecting potential alterations in meltwater runoff patterns. [ABSTRACT FROM AUTHOR]

Automated sea ice charting from synthetic aperture radar (SAR) has been researched for more than a decade, and we are still not close to unlocking the full potential of automated solutions in terms of resolution and accuracy. The central complications arise from ground truth data not being readily available in the polar regions. In this paper, we build a data set from 20 near-coincident x-band SAR acquisitions and as many airborne laser scanner (ALS) measurements from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC), between October and May. This data set is then used to assess the accuracy and robustness of five machine-learning-based approaches by deriving classes from the freeboard, surface roughness (standard deviation at 0.5 m correlation length) and reflectance. It is shown that there is only a weak correlation between the radar backscatter and the sea ice topography. Accuracies between 44 % and 66 % and robustness between 71 % and 83 % give a realistic insight into the performance of modern convolutional neural network architectures across a range of ice conditions over 8 months. It also marks the first time algorithms have been trained entirely with labels from coincident measurements, allowing for a probabilistic class retrieval. The results show that segmentation models able to learn from the class distribution perform significantly better than pixel-wise classification approaches by nearly 20 % accuracy on average. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

Semantic instance segmentation from scenes, serving as a crucial role for 3D modelling and scene understanding. Conducting semantic segmentation before grouping instances is adopted by the existing state-of-the-art methods. However, without additional refinement, semantic errors will fully propagate into the grouping stage, resulting in low overlap with the ground truth instance. Furthermore, the proposed methods focused on indoor level scenes, which are limited when directly applied to large-scale outdoor Airborne Laser Scanning (ALS) point clouds. Numerous instances, significant object density and scale variations make ALS point clouds distinct from indoor data. In order to address the problems, we proposed a geometric characterization-aware semantic instance segmentation network, which utilized both semantic and objectness score to select potential points for grouping. And in point cloud feature learning stage, hand-craft geometry features are taken as input for geometric characterization awareness. Moreover, to address errors propagated from previous modules after grouping, we have additionally designed a per-instance refinement module. To assess semantic instance segmentation, we conducted experiments on an open-source dataset. Additionally, we performed semantic segmentation experiments to evaluate the performance of our proposed point cloud feature learning method. [ABSTRACT FROM AUTHOR]

In recent years, power line inspections have benefited from the use of the lidar surveying technology, which enables safe and rapid data acquisition, even in challenging environments. To further optimize monitoring operations and reduce time and costs, automatic processing of the point clouds obtained is of greatest importance. This work presents a complete pipeline for processing power line data that includes (i) lidar point cloud segmentation using a Fully Convolutional Network, (ii) individual pylon identification via DBSCAN clustering, and (iii) the automatic extraction and modelling of any number of cables using a multi-model fitting algorithm based on the J-Linkage method. The proposed procedure is tested on a 36 km-long power line, resulting in a F1-score of 97.6% for pylons and 98.5% for the vectorized cables. [ABSTRACT FROM AUTHOR]

Forest canopy height data are crucial for estimating forest carbon storage and assessing forest ecology. By utilizing satellite imagery, canopy height data obtained from airborne or spaceborne LiDAR have been expanded from footprint and plot levels to spatially continuous elevation mapping of forests. However, current research suggests that estimating forest canopy height without forest type data presents a challenge in how to effectively integrate multi-source LiDAR data and ensure the samples adequately represent various forest types for higher estimation accuracy. Therefore, this study proposes a forest canopy height estimation method that considers forest structure and integrates multi-source LiDAR data to overcome the challenge. First, a stratified sampling method based on forest structure (SSMFS) was proposed to select training samples and enhance their representativeness. Second, we combined GEDI and ATL08 data to create a multi-source spaceborne LiDAR dataset, enhancing geographic coverage and increasing canopy height samples. Third, the spaceborne LiDAR-based canopy height estimation model incorporates previously unconsidered canopy openness features and uses SSMFS to select training samples. Finally, we improved spaceborne LiDAR canopy height accuracy by creating a residual correction model that adjusts for differences between airborne scanner (ALS) and spaceborne LiDAR estimates. This study, conducted in Zhangwu County, achieved an accuracy of R2 = 0.71, MAE = 1.20 m, and RMSE = 1.71 m. These results show a 51.06% increase in R2, a 26.38% decrease in MAE, and a 24.00% decrease in RMSE compared to recent research. In summary, this study profoundly amplifies predictive accuracy, providing a clear advantage in the delineation of regional forest canopy maps. [ABSTRACT FROM AUTHOR]

The forest structure of Canada's northern unmanaged boreal forests are not well characterized despite their critical importance for ecosystem services such as carbon storage and caribou habitat, as well as their vulnerability to climate change and increasing disturbance rates, especially wildfire. Spaceborne lidar observations acquired from the Advanced Topographic Laser Altimeter System (ATLAS) instrument onboard the Ice, Cloud and Elevation Satellite-2 (ICESat-2), enables monitoring of forest structure in these remote northern ecosystems and characterization of long-term post-fire recovery; however, ICESat-2 is a sampling instrument, acquiring data transects rather than wall-to-wall data coverage. By imputing ICESat-2 estimates of canopy height using annual time-series (1984–2021) of spectral indices, change metrics, and land cover metrics from Landsat surface reflectance composites, we derived annual, spatially explicit, wall-to-wall estimates of canopy height for the period 1984–2021 across a 19.6 Mha area of boreal forests in northwestern Ontario and a 630,000 ha area of managed boreal forest further south that had coincident airborne laser scanning (ALS) data for validation. The accuracy of the imputation of derived canopy height estimates in the northwestern site was assessed using a reserved set of ICESat-2 observations (r = 0.76; RMSD = 3.04 m, pRMSD = 33.9%, MD = −0.10 m, and pMD = −1.0%). Using coincident ALS data, the accuracy of the imputed canopy heights in the southern study site were also assessed (r = 0.81; RMSD = 3.65 m, pRMSD = 25.87%), MD = −0.70 m, pMD = 4.96%). Examination of height dynamics post fire indicated that canopy height decreased 8–10 years post fire and recovered between 60% and 85% of pre-fire canopy height within 30 years post fire. The approach presented could readily be extended to similar northern boreal forest areas to provide broad-scale characterizations of fire impacts on forest structure and subsequent recovery. [ABSTRACT FROM AUTHOR]

Personal laser scanning devices employing Simultaneous Localization and Mapping (SLAM) technology have rightfully gained traction in various applications, including forest mensuration and inventories. This study focuses the inherent stochastic noise in SLAM data. An analysis of noise distribution is performed in GeoSLAM ZEB Horizon for point clouds of trees of two species, Norway spruce and European beech, to mitigate bias in diameter estimates. The method involved evaluating residuals of individual 3D points concerning the real tree surface model based on TLS data. The results show that the noise is not symmetrical regarding the real surface, showing significant negative difference, and moreover, the difference from zero mean significantly differs between species, with an average of −0.40 cm for spruce and −0.44 cm for beech. Furthermore, the residuals show significant dependence on the return distance between the scanner and the target and the incidence angle. An experimental comparison of RANSAC circle fitting outcomes under various configurations showed unbiased diameter estimates with extending the inlier tolerance to 5 cm with 2.5 cm asymmetry. By showing the nonvalidity of the assumption of zero mean in diameter estimation methods, the results contribute to fill a gap in the methodology of data processing with the widely utilized instrument. [ABSTRACT FROM AUTHOR]

This study introduces an enhanced octree-based framework for integrated geotechnical analysis, combining geological, microseismic, and Mobile Laser Scanning (MLS)-based change detection data. Our approach leverages efficient statistical inference-based change detection techniques and additional octree data structures, enabling voxel-level data integration and association, semantic clustering, and comprehensive geotechnical analysis. We detail the implementation of our method, demonstrating capabilities such as integrating geological fault data, seismic energy exposure modeling, and combination of Random Sample Consensus (RANSAC) classification with Density-Based Spatial Clustering of Applications with Noise (DBSCAN). We conduct a suite of statistical analyses to investigate multi-dimensional trends and correlations among MLS-measured changes, distances to geological faults, and seismic energy exposure. We found that binary change classification and change magnitude negatively correlate with three investigated fault-distance metrics. Multivariant analysis reveals that increased seismic energy exposure and decreased fault distance positively correlates with MLS-based change classification and magnitudes. Our findings imply that proximity to geological faults and seismic energy exposure can be statistically linked to increases in geotechnically relevant deformations. This study is the first to demonstrate that octree-based MLS data processing can reveal multi-dimensional trends in underground mine datasets, thereby enhancing understanding of complex geomechanical behaviors crucial for mining productivity and safety. Highlights: Introduced octree-based framework for integrated geotechnical analysis. Combined geological, microseismic, and MLS data at voxel level. Utilized statistical inference for change detection in framework. Found correlations between MLS changes, fault proximity, and seismic energy. [ABSTRACT FROM AUTHOR]

Our image of the Roman landscape of Istria is characterised by large‐scale centuriation and architectural remains of Roman villae. Detection and mapping of other, less dramatic landscape features require systematic large‐scale prospection, but this faces significant difficulties in the Mediterranean environment. However, the developments in the field of airborne laser scanning offer the possibility to create archaeologically usable digital terrain models under water and under very dense and low maquis vegetation. This paper reports on the use of terrain models created using a green laser and a sophisticated archaeologically driven ground point filtering strategy. Combined with archaeological aerial photo interpretation, this provides the means for landscape mapping and interpretation that has revealed a wealth of archaeological structures hinting at Roman agricultural practices and landscape. Our case study is based on a laser scan of about 24 km2 of land and underwater terrain in Medulin Bay. Processed, visualised and interpreted for archaeological purposes, the data reveal not only features ranging from prehistoric hilltop settlements to modern military installations but also features a complex picture of the Roman land use. Of particular interest is the large number of planting pits, which extend over a total length of 4 km. They were laid on a regular grid of approximately 35 × 35 m, sometimes combined in contiguous parcels. They can be interpreted as remains of orchards or tree nurseries of Roman date, and the paper examines also the question of whether they can be linked to the associated Roman estates. The case study area presented here shows that the potential of remote sensing methods goes far beyond the mere finding of traditional sites but can open up new landscape‐scale perspectives on regions that have been archaeologically little explored. [ABSTRACT FROM AUTHOR]

In continuous cover forestry, plenter silviculture is regarded as an elaborated system for optimizing the sustainable production of high-quality timber maintaining a constant but heterogeneous canopy. Its complexity necessitates high silvicultural expertise and a detailed assessment of forest stand structural variables. Terrestrial laser scanning (TLS) can offer reliable techniques for long-term tree mapping, volume calculation, and stand variables assessment in complex forest structures. We conducted surveys using both automated TLS and conventional manual methods (CMM) on two plots with contrasting silvicultural regimes within the Black Forest, Germany. Variations in automated tree detection and stand variables were greater between different TLS surveys than with CMM. TLS detected an average of 523 tree stems per hectare, while CMM counted 516. Approximately 9.6% of trees identified with TLS were commission errors, with 6.5% of CMM trees being omitted using TLS. Basal area per hectare was slightly higher in TLS (38.9 m3) than in CMM (38.2 m3). However, CMM recorded a greater standing volume (492.7 m3) than TLS (440.5 m3). The discrepancy in stand volume between methods was primarily due to TLS underestimating tree height, especially for taller trees. DBH bias was minor at 1 cm between methods. Repeated TLS inventories successfully matched an average of 424 tree positions per hectare. While TLS adequately characterizes complex plenter forest structures, we propose enhancing this methodology with personal laser scanning to optimize crown coverage and efficiency and direct volume measurements for increased accuracy of wood volume estimations. Additionally, utilizing 3D point cloud data-derived metrics, such as structural complexity indices, can further enhance plenter forest management. [ABSTRACT FROM AUTHOR]

Change detection methods for measuring deformation of the LNG storage tank's exterior wall cannot meet the requirements for extensive and comprehensive deformation analysis. The method proposed in this paper uses multi‐source spatial data to detect the deformation of the LNG storage tank's exterior wall. Exterior wall data collected simultaneously by terrestrial and airborne laser scanners, as well as close‐range and oblique photogrammetry, are pre‐processed separately and then registered to the global point clouds. The deformation state of exterior wall is represented by a thorough assessment approach that takes into account four change indices. The results suggest that the registration precision of global point clouds is 6 mm. The global ovality of the LNG storage tank is 0.053%, with a tilt change of 2.58° and a deviation of 27 mm in the direction of the azimuth of 321.98°, an average value of 0.07 mm for depression and protrusion deformation, and a surface flatness of 21.3 mm. In this case, the flatness is greater than the variation threshold of the standard specification, indicating that the tank may have structural uneven settlement or other deformation problems. The attraction of the proposed method resides in its capacity to efficiently and accurately obtain spatial positioning and color information of the LNG storage tank's exterior wall, thereby providing a robust foundation for precise judgments regarding the deformation state. [ABSTRACT FROM AUTHOR]