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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]

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]

The leaf area index (LAI) is a critical variable for forest ecosystem processes. Passive optical and active LiDAR remote sensing have been used to retrieve LAI. LiDAR data have good penetration to provide vertical structure distribution and deliver the ability to estimate forest LAI, such as the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). Segment size and beam type are important for ICESat-2 LAI estimation, as they affect the amount of signal photons returned. However, the current ICESat-2 LAI estimation only covered a limited number of sites, and the performance of LAI estimation with different segment sizes has not been clearly compared. Moreover, ICESat-2 LAIs derived from strong and weak beams lack a comparative analysis. This study derived and evaluated LAI from ICESat-2 data over the National Ecological Observatory Network (NEON) sites in North America. The LAI estimated from ICESat-2 for different segment sizes (20, 100, and 200 m) and beam types (strong beam and weak beam) were compared with those from the airborne laser scanning (ALS) and the Copernicus Global Land Service (CGLS). The results show that the LAI derived from strong beams performs better than that of weak beams because more photon signals are received. The LAI estimated from the strong beam at the 200 m segment size shows the highest consistency with those from the ALS data (R = 0.67). Weak beams also present the potential to estimate LAI and have moderate agreement with ALS (R = 0.52). The ICESat-2 LAI shows moderate consistency with ALS for most forest types, except for the evergreen forest. The ICESat-2 LAI shows satisfactory agreement with the CGLS 300 m LAI product (R = 0.67, RMSE = 1.94) and presents a higher upper boundary. Overall, the ICESat-2 can characterize canopy structural parameters and provides the ability to estimate LAI, which may promote the LAI product generated from the photon-counting LiDAR. [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]

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]

Canopy cover (CC) is the proportion of a forest floor covered by the vertical projection of tree crowns. Recently, it has become common to utilize LiDAR (light detection and ranging) canopy metrics to estimate CC over large areas. However, these metrics are primarily related to canopy density rather than the specific definition of CC. Here, two processes that employed individual tree segmentation (ITS) and area-based procedures based on LiDAR data are presented to estimate CC across the Talladega Division of the Talladega National Forest (93,694 ha) at the plot, stand, and landscape levels. The two analytical procedures were assessed using the results of a plot/grid method as a reference dataset, which focused on CC estimates within 255 field measurement fixed-area sample plots. The accuracy of a third process, employing an imagery-based visual CC assessment, was also compared against the two procedures and the reference dataset. The LiDAR-based analytical procedures were able to provide estimates of CC with an RMSE of approximately 15 %, which is acceptable for landscape-level assessments. Based on the results of this study, we conclude that CC maps, when created using LiDAR data, may be suitable for various operational tasks such as assessing the impact of forest disturbances and helping to determine the habitat suitability for certain wildlife species.

This study presents a new approach for predicting forest aboveground biomass (AGB) from airborne laser scanning (ALS) data: AGB is predicted from sequences of images depicting vertical cross-sections through the ALS point clouds. A 3D version of the VGG16 convolutional neural network (CNN) with initial weights transferred from pre-training on the ImageNet dataset was used. The approach was tested on datasets from Canada, Poland, and the Czech Republic. To analyse the effect of training sample size on model performance, different-sized samples ranging from 10 to 375 ground plots were used. The CNNs were compared with random forest models (RFs) trained on point cloud metrics. At the maximum number of training samples, the difference in RMSE between observed and predicted AGB of CNNs and RFs ranged from −2 t/ha to 5 t/ha, and the difference in squared Pearson correlation coefficient ranged from −0.05 to 0.06. Additional pre-training on synthetic data derived from virtual laser scanning of simulated forest stands could only improve the prediction performance of the CNNs when only a few real training samples (10–40) were available. While 3D CNNs trained on cross-section images derived from real data showed promising results, RFs remain a competitive alternative.

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.

Understanding forest health is of great importance for the conservation of the integrity of forest ecosystems. The monitoring of forest health is, therefore, indispensable for the long-term conservation of forests and their sustainable management. In this regard, evaluating the amount and quality of dead wood is of utmost interest as they are favorable indicators of biodiversity. Apparently, remote sensing-based Machine Learning (ML) techniques have proven to be more efficient and sustainable with unprecedented accuracy in forest inventory. However, the application of these techniques is still in its infancy with respect to dead wood mapping. This study, for the first time, automatically categorizing individual coniferous trees (Norway spruce) into five decay stages (live, declining, dead, loose bark, and clean) from combined Airborne Laser Scanning (ALS) point clouds and color infrared (CIR) images using three different ML methods − 3D point cloud-based deep learning (KPConv), Convolutional Neural Network (CNN), and Random Forest (RF). First, CIR colorized point clouds are created by fusing the ALS point clouds and color infrared images. Then, individual tree segmentation is conducted, after which the results are further projected onto four orthogonal planes. Finally, the classification is conducted on the two datasets (3D multispectral point clouds and 2D projected images) based on the three ML algorithms. All models achieved promising results, reaching overall accuracy (OA) of up to 88.8%, 88.4% and 85.9% for KPConv, CNN and RF, respectively. The experimental results reveal that color information, 3D coordinates, and intensity of point clouds have significant impact on the promising classification performance. The performance of our models, therefore, shows the significance of machine/deep learning for individual tree decay stages classification and landscape-wide assessment of the dead wood amount and quality by using modern airborne remote sensing techniques. The proposed method can contribute as an important and reliable tool for monitoring biodiversity in forest ecosystems.

Abstract Simulating realistic three‐dimensional (3D) forest scenes is useful in understanding the links between forest structure and ecosystem functions (e.g. radiative transfer). Light detection and ranging (LiDAR) technology provides useful 3D data for forest reconstructions since it can characterise 3D structures of individual trees and canopies. High‐density terrestrial LiDAR (terrestrial laser scanning, TLS) is suitable for fine‐scale reconstructions but is limited to smaller forest plots; low‐density airborne LiDAR (airborne laser scanning, ALS) can cover larger areas but is only suitable for coarse‐scale reconstructions. How to take advantage of TLS and ALS to enable fine‐scale forest simulations in large areas needs to be studied. We propose a new voxel‐based method for forest simulations using the integration of TLS and ALS data. TLS data of representative reference trees are used to approximate the detailed architectures of the whole forest scene, with structural information on each individual tree extracted from ALS data. The high‐density point cloud data derived from TLS and ALS data are voxelised using high resolution solid voxels for scene representation. We tested the proposed method using two virtual forests (108 m × 108 m) and a real forest (300 m × 300 m) with conifer and broadleaf species. The physically based ray tracer (PBRT) was used to visualise the true virtual forest scenes, whereas voxel‐based radiative transfer (VBRT) was used to visualise the modelled forest scenes from LiDAR data. For the real forest scene, simulated and real ALS data were compared. Our results demonstrate that the images simulated by VBRT and PBRT are similar in the virtual forest scenes, with average radiance values of 1.02 and 1.72, respectively. In the real forest scene, the distributions of points and individual tree attributes (tree height, crown radius, and tree volume) derived from real and simulated ALS also match well, with Kullback–Leibler divergence ranging from 0.006 to 0.06. We conclude that the new method is capable of modelling fine‐scale 3D forests in large areas (over 1 ha) when TLS and ALS data are available, and it has good potential in studying the process of radiative transfer in conifer and broadleaf forests.

Simulating realistic three‐dimensional (3D) forest scenes is useful in understanding the links between forest structure and ecosystem functions (e.g. radiative transfer). Light detection and ranging (LiDAR) technology provides useful 3D data for forest reconstructions since it can characterise 3D structures of individual trees and canopies. High‐density terrestrial LiDAR (terrestrial laser scanning, TLS) is suitable for fine‐scale reconstructions but is limited to smaller forest plots; low‐density airborne LiDAR (airborne laser scanning, ALS) can cover larger areas but is only suitable for coarse‐scale reconstructions. How to take advantage of TLS and ALS to enable fine‐scale forest simulations in large areas needs to be studied.We propose a new voxel‐based method for forest simulations using the integration of TLS and ALS data. TLS data of representative reference trees are used to approximate the detailed architectures of the whole forest scene, with structural information on each individual tree extracted from ALS data. The high‐density point cloud data derived from TLS and ALS data are voxelised using high resolution solid voxels for scene representation. We tested the proposed method using two virtual forests (108 m × 108 m) and a real forest (300 m × 300 m) with conifer and broadleaf species. The physically based ray tracer (PBRT) was used to visualise the true virtual forest scenes, whereas voxel‐based radiative transfer (VBRT) was used to visualise the modelled forest scenes from LiDAR data. For the real forest scene, simulated and real ALS data were compared.Our results demonstrate that the images simulated by VBRT and PBRT are similar in the virtual forest scenes, with average radiance values of 1.02 and 1.72, respectively. In the real forest scene, the distributions of points and individual tree attributes (tree height, crown radius, and tree volume) derived from real and simulated ALS also match well, with Kullback–Leibler divergence ranging from 0.006 to 0.06.We conclude that the new method is capable of modelling fine‐scale 3D forests in large areas (over 1 ha) when TLS and ALS data are available, and it has good potential in studying the process of radiative transfer in conifer and broadleaf forests. [ABSTRACT FROM AUTHOR]

This study introduces an automated, open-source workflow for large-scale 2D and 3D building mapping using airborne LiDAR data. Uniquely, our workflow operates entirely unsupervised, eliminating the need for any training procedures. We have integrated a specially tailored digital terrain model generation algorithm into our workflow to prevent errors in complex urban landscapes, especially around highways and overpasses. Through fine rasterization of LiDAR point clouds, we have enhanced building-tree differentiation. In addition, we have reduced errors near water bodies and augmented computational efficiency by introducing a new planarity calculation. Our workflow offers a practical and scalable solution for the mass production of rasterized 2D and 3D building maps from raw airborne LiDAR data. Our method's robustness has been rigorously validated across a diverse dataset in comparison with deep learning-based and hand-digitized products. Through these extensive comparisons, we provide a valuable analysis of building maps generated via different methodologies. We anticipate that our highly scalable building mapping workflow will facilitate the production of reliable 2D and 3D building maps, fostering advances in large-scale urban analysis.

Semantic segmentation of airborne point clouds is crucial for 3D scene reconstruction and remote sensing in surveying applications. Current deep learning methods for point clouds primarily focus on effectively aggregating local neighborhood information. However, they often overlook the fusion of global context information and elevation features, which are vital for airborne point clouds. In this study, we propose Dense-LGEANet, a novel network with dense connected architecture and multiscale feature supervision based on our designed LGEA module. The key component of our LGEA module is the combination of the graph convolution block and the transformer block with elevation attention. It can effectively fuse local neighborhood information and global context information to improve the accuracy of semantic segmentation of airborne point cloud. Moreover, the designed dense connected network architecture can enhance the feature extraction capability for point cloud objects at different scales by facilitating interactions between multiple up-sampling and down-sampling layers. We have conducted multiple experiments on the public point cloud dataset. Experimental results show that our method can achieve an mIoU of 58.5% and an mF1 of 72.0% on the ISPRS Vaihingen 3D dataset, while an mIoU of 67.2% and an mF1 of 78.3% on the LASDU dataset. It demonstrates the superior performance of our network and the effectiveness of the proposed feature enhancement module and network architecture.

The integration of geodetic and photogrammetric data has become a new tool that has expanded the existing measurement capabilities, as well as it found its application outside the geodetic sector. As a result, over the past decades, the process of topographic data acquisition has caused cartographic industry to move from classical surveying methods to passive and active detection methods. The introduction of remote sensing technology has not only improved the speed of data acquisition but has also provided elevation data for areas that are difficult to access and survey. The aim of the work is to analyse consistency of elevation data from the Georeference Database of Topographic Objects (Pol. Baza danych obiektów topograficznych – BDOT500) with data from airborne laser scanning (ALS) for selected 15 research areas located in the City of Kraków. The main findings reveal discrepancies between elevation data sources, potentially affecting the accuracy of various applications, such as flood risk assessment, urban planning, and environmental management. The research gap identified in the study might stem from the lack of comprehensive investigations into the consistency and accuracy of elevation data across different databases and technologies in urban areas. This gap highlights the need for a thorough examination of the reliability of various data sources and methods of urban planning, disaster management, and environmental analysis. The integration of diverse databases and technologies, like ALS and geodetic measurements, in various applications introduces potential discrepancies that can significantly impact decision-making and outcomes.

The leaf area index (LAI) is a critical variable for forest ecosystem processes. Passive optical and active LiDAR remote sensing have been used to retrieve LAI. LiDAR data have good penetration to provide vertical structure distribution and deliver the ability to estimate forest LAI, such as the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). Segment size and beam type are important for ICESat-2 LAI estimation, as they affect the amount of signal photons returned. However, the current ICESat-2 LAI estimation only covered a limited number of sites, and the performance of LAI estimation with different segment sizes has not been clearly compared. Moreover, ICESat-2 LAIs derived from strong and weak beams lack a comparative analysis. This study derived and evaluated LAI from ICESat-2 data over the National Ecological Observatory Network (NEON) sites in North America. The LAI estimated from ICESat-2 for different segment sizes (20, 100, and 200 m) and beam types (strong beam and weak beam) were compared with those from the airborne laser scanning (ALS) and the Copernicus Global Land Service (CGLS). The results show that the LAI derived from strong beams performs better than that of weak beams because more photon signals are received. The LAI estimated from the strong beam at the 200 m segment size shows the highest consistency with those from the ALS data (R = 0.67). Weak beams also present the potential to estimate LAI and have moderate agreement with ALS (R = 0.52). The ICESat-2 LAI shows moderate consistency with ALS for most forest types, except for the evergreen forest. The ICESat-2 LAI shows satisfactory agreement with the CGLS 300 m LAI product (R = 0.67, RMSE = 1.94) and presents a higher upper boundary. Overall, the ICESat-2 can characterize canopy structural parameters and provides the ability to estimate LAI, which may promote the LAI product generated from the photon-counting LiDAR.

Airborne laser scanning (ALS) data has been widely used for total aboveground tree biomass (AGB) modelling, however, there is less research focusing on estimating specific tree biomass components (wood, branches, bark, and foliage). Knowledge about these biomass components is essential for carbon accounting, understanding forest nutrient cycling, and other applications. In this study, we compare additive AGB estimation (sum of estimated components) with direct AGB estimation using deep neural network (DNN) and random forest (RF) models. We utilise two point cloud DNNs: point-based Dynamic Graph Convolutional Neural Network (DGCNN) and Octree-based Convolutional Neural Network (OCNN). DNN and RF models were trained using a dataset comprised of 2336 sample plots from a mixed temperate forest in New Brunswick, Canada. Results indicate that additive AGB models perform similarly to direct models in terms of coefficient of determination (R2) and root-mean square error (RMSE), and reduced the mean absolute percentage error (MAPE) by 22% on average. Compared to RF, the DNNs provided a small improvement in performance, with OCNN explaining 5% more variation in the data (R2 = 0.76) and reducing MAPE by 20% on average. Overall, this study showcases the effectiveness of additive tree AGB models and highlights the potential of DNNs for enhanced AGB estimation. To further improve DNN performance, we recommend using larger training datasets, implementing hyperparameter optimization, and incorporating additional data such as multispectral imagery.

Leaf area index (LAI) is defined as one half of the total green leaf area per unit ground surface area. Its vertical profile is critical for understanding the remote sensing radiative transfer processes. LAI profile has been derived from airborne and spaceborne LiDAR data, such as the Global Ecosystem Dynamics Investigation (GEDI) installed on the International Space Station. However, the capability of various algorithms for the LAI profile estimation with airborne LiDAR is not clearly evaluated, and the estimated LAI profiles, including the GEDI LAI products, are not been fully validated. This study conducted a quantitative retrieval and validation of the LAI profiles using terrestrial and airborne laser scanning (TLS and ALS) and spaceborne GEDI data over a deciduous needleleaf forest site in northern China. The vertical LAI profile was estimated in the field using an upward digital hemispherical photography (DHP) attached to a portable measurement system in 2020 and 2021. A suite of new LiDAR indices combining both LiDAR return number and return intensity was explored for the LAI profile estimation. All LAI profiles obtained from the DHP, TLS, ALS, and GEDI during the leaf-on season and leaf-off season were compared. The DHP shows a good agreement with the TLS LAI profiles (R2 = 0.97). The LAI profile derived from the ALS data using the combined light penetration index (LPIRI) agrees well (R2 ≥0.86) with the DHP, TLS, and GEDI estimates. In general, the LPIRI is advantageous for regional LAI profile mapping from ALS. The GEDI cumulative LAI corresponds well with the DHP during the leaf-on season (R2 = 0.90, RMSE = 0.23), but underestimates during the leaf-off season (R2 = 0.70, RMSE = 0.14, bias=−0.13). The underestimation is attributed to the higher canopy and ground reflectance ratio (ρv/ρg) assigned in the algorithm and the height discrepancy between the GEDI and field measurements. For the GEDI LAI profile product, further validation and improvement are necessary for other biome types and landscape conditions, especially during the leaf-off season.

The identification and delineation, through aerial photography, of the archaeological structures that present temporal resolution, as well as their characterization based on high-resolution LiDAR (Light Detection and Ranging)-derived DEMs (Digital Elevation Models) are modern techniques widely used in the archaeological prospecting of various landscapes. In this study, we present an application of Airborne Laser Scanning (ALS) and aerial photography (AP) techniques, used in order to compute geomorphometric indices specific to the ashmound structures of Late Bronze Age (LBA) archaeological sites that are visible on the soil surface. The necessity of determining the ashmounds' geoarchaeological description stems from the fact that despite the majority of archaeologists weighing in on the subject, there is still no accepted explanation regarding their initial functionality. Thus, we believe that the GIS-based high-resolution characterization of 200 ashmound features identified in 21 Noua Culture (NC) archaeological sites will contribute to a better understanding of the ashmounds' functionality and evolution in the heterogeneous landscape of the study area (NE Romania). Therefore, various shape indices, such as the area (A), perimeter (P), length (L), form factor (RF), circularity ratio (RC), and elongation ratio (RE) were computed for microlevel characterizations of the visible ashmounds' structures. Additionally, LiDAR-derived DEMs with a 0.5 m resolution were used to generate more surface characteristics such as the slope (S) and hypsometric indices (HI). The outcomes indicate that the ashmounds have relatively diverse shapes (an RF range from 0.37 to 0.77; a RC range from 0.79 to 0.99; a RE range from 0.68 to 0.99), and the micro-relief slightly varies from positive to negative landforms (HI range from 0.34 to 0.61) depending on the erosion intensity (S range from 1.17° to 19.69°) and anthropogenic impact (e.g., current land use and agriculture type). Furthermore, each morphometric parameter is an indicator for surface processes, aiding in the identification of the geomorphologic and surface-erosion aspects that affect the archaeological remains, contributing to the assessment of the conservation status of the ashmound structures within the current landscape configuration. In this regard, this article presents and discusses the remote sensing (RS) techniques used, as well as the morphometric data obtained, exploring the implications of our findings for a better characterization of the NC in Romania. [ABSTRACT FROM AUTHOR]

The integration of geodetic and photogrammetric data has become a new tool that has expanded the existing measurement capabilities, as well as it found its application outside the geodetic sector. As a result, over the past decades, the process of topographic data acquisition has caused cartographic industry to move from classical surveying methods to passive and active detection methods. The introduction of remote sensing technology has not only improved the speed of data acquisition but has also provided elevation data for areas that are difficult to access and survey. The aim of the work is to analyse consistency of elevation data from the Georeference Database of Topographic Objects (Pol. Baza danych obiektów topograficznych -- BDOT500) with data from airborne laser scanning (ALS) for selected 15 research areas located in the City of Kraków. The main findings reveal discrepancies between elevation data sources, potentially affecting the accuracy of various applications, such as flood risk assessment, urban planning, and environmental management. The research gap identified in the study might stem from the lack of comprehensive investigations into the consistency and accuracy of elevation data across different databases and technologies in urban areas. This gap highlights the need for a thorough examination of the reliability of various data sources and methods of urban planning, disaster management, and environmental analysis. The integration of diverse databases and technologies, like ALS and geodetic measurements, in various applications introduces potential discrepancies that can significantly impact decision-making and outcomes. [ABSTRACT FROM AUTHOR]

Accurate estimation of above-ground biomass (AGB) in forested areas is essential for studying forest ecological functions, surface carbon cycling, and global carbon balance. Over the past decade, models that harness the distinct features of multi-source remote sensing observations for estimating AGB have gained significant popularity. It is worth exploring the differences in model performance by using simple and fused data. Additionally, quantitative estimation of the impact of high-cost laser point clouds on satellite imagery of varying costs remains largely unexplored. To address these challenges, model performance and cost must be considered comprehensively. We propose a comprehensive assessment based on three perspectives (i.e., performance, potential and limitations) for four typical AGB-estimation models. First, different variables are extracted from the multi-source and multi-resolution data. Subsequently, the performance of four regression methods is tested for AGB estimation with diverse indicator combinations. Experimental results prove that the combination of multi-source data provides a highly accurate AGB regression model. The proposed regression and variables rating approaches can flexibly integrate other data sources for modeling. Furthermore, the data cost is discussed against the AGB model performance. Our study demonstrates the potential of using low-cost satellite data to provide a rough AGB estimation for larger areas, which can allow different remote sensing data to meet different needs of forest management decisions.

Airborne laser scanning (ALS) and terrestrial laser scanning (TLS) has attracted attention due to their forest parameter investigation and research applications. ALS is limited to obtaining fine structure information below the forest canopy due to the occlusion of trees in natural forests. In contrast, TLS is unable to gather fine structure information about the upper canopy. To address the problem of incomplete acquisition of natural forest point cloud data by ALS and TLS on a single platform, this study proposes data registration without control points. The ALS and TLS original data were cropped according to sample plot size, and the ALS point cloud data was converted into relative coordinates with the center of the cropped data as the origin. The same feature point pairs of the ALS and TLS point cloud data were then selected to register the point cloud data. The initial registered point cloud data was finely and optimally registered via the iterative closest point (ICP) algorithm. The results show that the proposed method achieved high-precision registration of ALS and TLS point cloud data from two natural forest plots of Pinus yunnanensis Franch. and Picea asperata Mast. which included different species and environments. An average registration accuracy of 0.06 m and 0.09 m were obtained for P. yunnanensis and P. asperata, respectively. [ABSTRACT FROM AUTHOR]

Archaeological LiDAR has evolved into an indispensable component of archaeological prospection and landscape archaeology. However, it is frequently employed as a black-box digital method, which confines it to the realm of a specialized field. Making scientific publications more accessible, transparent, and reproducible is one of the steps required to turn LiDAR into a background method for all archaeologists. This is possible, according to our proposal, through the Executable Map Paper concept. This concept can be understood as a type of executable paper that pursues Open Science’s goals. The proposed technical solution consists of a PDF frontend, a persistence layer, and a hyperlinked interactive map. Executable Map Paper is applicable to all map-dependent sciences, including geography, geology, and any geoscience. In this paper, we outline the theoretical context, propose technical solutions, and provide a practical illustration.

The vertical structure of forests provides important parameters for estimating aboveground biomass (AGB) and it can be measured by lidar sensors. The Global Ecosystem Dynamics Investigation (GEDI) full-waveform lidar sensor collects data systematically over the Earth's surface from the International Space Station. Since GEDI became operational, it has collected billions of ∼25-m diameter footprints. This massive dataset has been used to create higher level gridded and non-grided products. However, GEDI's ∼25-m footprints can be subject to errors associated with effects of geolocation, terrain slope, and beam sensitivity, among others, which are likely transferred to the downstream products. This study aims to (1) evaluate the effect of beam sensitivity and terrain slope on the accuracy of relative heights (RH) of GEDI product L2A version 2 through comparison with discrete-return airborne lidar data collected over transects in the Brazilian Amazon Forest biome, (2) assess GEDI's geolocation uncertainty and investigate its combined effects with beam sensitivity and terrain slope, and (3) re-evaluate beam sensitivity and terrain slope effects on the GEDI L2A RHs using footprints that were geolocation-adjusted through a simple novel approach. The analysis separates GEDI footprints by acquisition time, i.e., daytime, nighttime, and combined (all-data). The discrete-return airborne lidar point clouds are used to derive terrain slope within the GEDI footprints and to simulate GEDI waveforms and derive RHs comparable to the GEDI L2A product. Results indicate that terrain slope only causes significant effects on GEDI data collected during daytime because solar radiation affects waveform signal-to-noise ratio. Beam sensitivity causes significant effects on nighttime and all-data GEDI L2A RHs. If geolocation uncertainty is considered, the effects of beam sensitivity and terrain slope have just minor changes. Geolocation-adjusted data continue showing significant effects on nighttime and all-data RH differences caused by beam sensitivity but produce unbiased results. This study improves the understanding of how beam sensitivity, terrain slope, and their combined effect with geolocation uncertainty may affect the GEDI L2A RH collected over the Brazilian Amazon forest during daytime and nighttime.

The paper investigates the usage of LiDAR (light detection and ranging) data for the automation of mapping vegetation with respect to the evaluation of the ecological succession process. The study was performed for the repository of the "Fryderyk" mine (southern Poland). The post-flotation area analyzed is a unique refuge habitat—Natura2000, PLH240008—where a forest succession has occurred for several dozen years. Airborne laser scanning (ALS) point clouds were used for deriving detailed information about the morphometry of the spoil heap and about the secondary forest succession process—mainly vegetation parameters i.e., height and canopy cover. The area of the spoil heap is irregular with a flat top and steep slopes above 20°. Analyses of ALS point clouds (2011 and 2019), confirmed progression in the forest succession process, and land cover changes especially in wooded or bushed areas. Precise vegetation parameters (3D LiDAR metrics) were calculated and provided the following parameters: mean value of vegetation height as 6.84 m (2011) and 8.41 m (2019), and canopy cover as 30.0% (2011) and 42.0% (2019). Changes in vegetation volume (3D area) were shown: 2011—310,558 m3, 2019—325,266 m3, vegetation removal—85,136 m3, increasing ecological succession—99,880 m3. [ABSTRACT FROM AUTHOR]

Archaeological LiDAR has evolved into an indispensable component of archaeological prospection and landscape archaeology. However, it is frequently employed as a black-box digital method, which confines it to the realm of a specialized field. Making scientific publications more accessible, transparent, and reproducible is one of the steps required to turn LiDAR into a background method for all archaeologists. This is possible, according to our proposal, through the Executable Map Paper concept. This concept can be understood as a type of executable paper that pursues Open Science's goals. The proposed technical solution consists of a PDF frontend, a persistence layer, and a hyperlinked interactive map. Executable Map Paper is applicable to all map-dependent sciences, including geography, geology, and any geoscience. In this paper, we outline the theoretical context, propose technical solutions, and provide a practical illustration. [ABSTRACT FROM AUTHOR]

Precise assessment of forest inventory can help optimize timber value and industrial forestmanagement planning. Light detection and ranging (LiDAR) technology has been recently introduced in forestry to effectively and precisely estimate and monitor forest inventories. In this study, we investigated the opportunities and limitations of integrated mobile laser scanning (MLS) and airborne laser scanning (ALS) LiDAR applications for estimating individual tree diameter at breast height (DBH) and tree height. Three circular fitting algorithms (integrated RANSAC and circle fitting, minimum enclosing circle, and least-squares ellipse fitting) were used to estimate DBH. Height was calculated using the crown height model (CHM). DBH was most accurately estimated by the ellipse-fitting algorithm. Integrated MLS and ALS performed better than previous circular fitting applications in estimating DBH values. Lastly, the tree height estimated using the CHM was compared with the ground truth, and R2 and the root mean square error (RMSE) of the height were 0.60 and 2.0 m, respectively. In addition, the trend of low accuracy for suppressed trees was identified from point cloud data (PCD) extraction because of overlapped PCD among the surrounding dominant trees. This result indicated that the segmentation of the top of suppressed trees was limited by overlap issues in high-density forests. [ABSTRACT FROM AUTHOR]

• Canopy cover is the proportion of the forest floor covered by the vertical projection of the tree crowns. • A framework is presented for creating canopy cover maps using only aerial LiDAR data. • Both individual tree- and area-based approaches are capable of creating sufficiently accurate canopy cover maps. • Canopy cover maps can be instrumental for various operational tasks in forestry, such as fire and habitat management. Canopy cover (CC) is the proportion of a forest floor covered by the vertical projection of tree crowns. Recently, it has become common to utilize LiDAR (light detection and ranging) canopy metrics to estimate CC over large areas. However, these metrics are primarily related to canopy density rather than the specific definition of CC. Here, two processes that employed individual tree segmentation (ITS) and area-based procedures based on LiDAR data are presented to estimate CC across the Talladega Division of the Talladega National Forest (93,694 ha) at the plot, stand, and landscape levels. The two analytical procedures were assessed using the results of a plot/grid method as a reference dataset, which focused on CC estimates within 255 field measurement fixed-area sample plots. The accuracy of a third process, employing an imagery-based visual CC assessment, was also compared against the two procedures and the reference dataset. The LiDAR-based analytical procedures were able to provide estimates of CC with an RMSE of approximately 15 %, which is acceptable for landscape-level assessments. Based on the results of this study, we conclude that CC maps, when created using LiDAR data, may be suitable for various operational tasks such as assessing the impact of forest disturbances and helping to determine the habitat suitability for certain wildlife species. [ABSTRACT FROM AUTHOR]

This study derives regression models for aboveground biomass (AGB) estimation in miombo woodlands of Tanzania that utilize the high availability and low cost of Sentinel-1 data. The limited forest canopy penetration of C-band SAR sensors along with the sparseness of available ground truth restricts their usefulness in traditional AGB regression models. Therefore, we propose to use AGB predictions based on airborne laser scanning (ALS) data as a surrogate response variable for SAR data. This dramatically increases the available training data and opens for flexible regression models that capture fine-scale AGB dynamics. This becomes a sequential modeling approach, where the first regression stage has linked in situ data to ALS data and produced the AGB prediction map; we perform the subsequent stage, where this map is related to Sentinel-1 data. We develop a traditional, parametric regression model and alternative nonparametric models for this stage. The latter uses a conditional generative adversarial network (cGAN) to translate Sentinel-1 images into ALS-based AGB prediction maps. The convolution filters in the neural networks make them contextual. We compare the sequential models to traditional, nonsequential regression models, all trained on limited AGB ground reference data. Results show that our newly proposed nonsequential Sentinel-1-based regression model performs better quantitatively than the sequential models, but achieves less sensitivity to fine-scale AGB dynamics. The contextual cGAN-based sequential models best reproduce the distribution of ALS-based AGB predictions. They also reach a lower RMSE against in situ AGB data than the parametric sequential model, indicating a potential for further development.

In order to explore the influence of different feature selection methods and estimation models on canopy closure estimation of natural secondary forests, 63 (0. 09 hm²) sample plots in Maoershan Experimental Forest Farm were taken as the research area, and Airborne Laser Scanning (ALS) data was used to extract features. Three feature selection methods (Pearson correlation analysis, Random Forest (RF) and Boruta)) and three models (Partial Least Squares Regression (PLSR), Random Forest Regression (RFR) and Support Vector Regression (SVR)) were employed to estimate canopy closure, and the accuracy evaluation and analysis of variance (ANOVA) of the model were carried out by fisheye photo method and sample method. The results showed that the accuracy of PLSR was the highest and SVR was the lowest. The estimation accuracy of RF and Boruta algorithm was higher than Pearson correlation analysis. The PLSR model after Boruta feature selection had the highest accuracy (R² = 0. 451 1, RMSE = 0. 067 5). The feature selection of fisheye photo and sample method, model fitting method of the fisheye photo had no significant effect, yet the model of sample method had a significant effect on estimating canopy closure. The results indicated that the accuracy of PLSR was better than others. The model of the sample method had a significant effect on canopy closure. ALS can effectively estimate the canopy closure of natural secondary forests, which provided a basis for large-scale estimation. [ABSTRACT FROM AUTHOR]

Digital aerial photogrammetry (DAP) has emerged as an alternative to airborne laser scanning (ALS) for forest inventory applications, as it offers a low-cost and flexible three-dimensional (3D) point cloud. Unlike the forest inventory attributes (e.g., tree height and diameter at breast height), the relative ability of DAP and ALS in predicting canopy structural variables (i.e., canopy cover and leaf area index (LAI)) has not been sufficiently investigated by previous studies. In this study, we comprehensively compared the canopy cover and LAI estimates using DAP- and ALS-based methods over 166 selected tropical forest sample plots with seven different tree species and forest types. We also explored the relationship between field-measured aboveground biomass (AGB) and the LAI estimates. The airborne LAI estimates were subsequently compared with the Sentinel-2-based LAI values that were retrieved using a one-dimensional radiative transfer model. The results demonstrated that the DAP-based method generally overestimated the two canopy variables compared to ALS-based methods but with relatively high correlations regardless of forest type and species (R2 of 0.80 for canopy cover and R2 of 0.76 for LAI). Under different forest types and species, the R2 of canopy cover and LAI range from 0.64 to 0.89 and from 0.54 to 0.87, respectively. Apparently, different correlations between AGB and LAI were found for different forest types and species where the mixed coniferous and broad-leaved forest shows the best correlation with R2 larger than 0.70 for both methods. The comparison with satellite retrievals verified that the ALS-based estimates are more consistent with Sentinel-2-based estimates than DAP-based estimates. We concluded that DAP data failed to provide analogous results to ALS data for canopy variable estimation in tropical forests. [ABSTRACT FROM AUTHOR]

Forests play a central role in the management of the Earth's climate. Airborne laser scanning (ALS) technologies facilitate the monitoring of large and impassable areas and can be used to monitor the 3D structure of forests. While the ALS-based forest measures have been studied in depth, 3D change detection in forests is still a subject of little attention in the literature due to the challenges introduced by comparing point cloud pairs. In this study, we propose an innovative methodology to (i) automatically perform a 3D change detection of forests on an individual tree level; (ii) estimate tree parameters with allometric equations; and (iii) perform an assessment of the aboveground biomass (AGB) variation over time. The area in which the tests were carried out was hit by an ice storm that occurred in the time interval between the two LiDAR acquisitions; furthermore, field measurements were carried out and used to validate the results. The single-tree segmentation of the point clouds was automatically performed with a local maxima algorithm to detect the treetop, and a decision tree method to define the individual crowns around the local maxima. The multitemporal comparison of the point clouds was based on the identification of single trees, which were matched when there was a correlation between the position of the treetops. For each tree, the DBH (diameter at breast height) and the AGB were also estimated using allometric equations. The results are promising and allowed us to identify the uprooted trees and estimate that about 40% of the AGB of the area under examination had been destroyed, with an RMSE over the estimation ranging between 4% and 21% in four scenarios. [ABSTRACT FROM AUTHOR]

The identification and delineation, through aerial photography, of the archaeological structures that present temporal resolution, as well as their characterization based on high-resolution LiDAR (Light Detection and Ranging)-derived DEMs (Digital Elevation Models) are modern techniques widely used in the archaeological prospecting of various landscapes. In this study, we present an application of Airborne Laser Scanning (ALS) and aerial photography (AP) techniques, used in order to compute geomorphometric indices specific to the ashmound structures of Late Bronze Age (LBA) archaeological sites that are visible on the soil surface. The necessity of determining the ashmounds’ geoarchaeological description stems from the fact that despite the majority of archaeologists weighing in on the subject, there is still no accepted explanation regarding their initial functionality. Thus, we believe that the GIS-based high-resolution characterization of 200 ashmound features identified in 21 Noua Culture (NC) archaeological sites will contribute to a better understanding of the ashmounds’ functionality and evolution in the heterogeneous landscape of the study area (NE Romania). Therefore, various shape indices, such as the area (A), perimeter (P), length (L), form factor (RF), circularity ratio (RC), and elongation ratio (RE) were computed for microlevel characterizations of the visible ashmounds’ structures. Additionally, LiDAR-derived DEMs with a 0.5 m resolution were used to generate more surface characteristics such as the slope (S) and hypsometric indices (HI). The outcomes indicate that the ashmounds have relatively diverse shapes (an RF range from 0.37 to 0.77; a RC range from 0.79 to 0.99; a RE range from 0.68 to 0.99), and the micro-relief slightly varies from positive to negative landforms (HI range from 0.34 to 0.61) depending on the erosion intensity (S range from 1.17° to 19.69°) and anthropogenic impact (e.g., current land use and agriculture type). Furthermore, each morphometric parameter is an indicator for surface processes, aiding in the identification of the geomorphologic and surface-erosion aspects that affect the archaeological remains, contributing to the assessment of the conservation status of the ashmound structures within the current landscape configuration. In this regard, this article presents and discusses the remote sensing (RS) techniques used, as well as the morphometric data obtained, exploring the implications of our findings for a better characterization of the NC in Romania.

Continuous mapping of vegetation height is critical for many forestry applications, such as planning vegetation management in power transmission line right-of-way. Satellite images from different sensors, including SAR (Synthetic Aperture Radar) from Sentinel 1 (S1) and multispectral from Sentinel 2 (S2), can be used for producing high-resolution vegetation height maps at a broad scale. The main objective of this study is to assess the potential of S1 and S2 satellite data, both in a single and a multisensor approach, for modeling canopy height in a transmission line right-of-way located in the Atlantic Forest of Paraná, Brazil. For integrating S1 and S2 data, we used three machine learning algorithms (LR: Linear Regression, CART: Classification and Regression Trees, and RF: Random Forest) and airborne LiDAR (Light Detection and Ranging) measurements as the reference height. The best models were obtained using the RF algorithm and 20 m resolution features from only S2 data (cross-validated RMSE of 4.92 m and R2 of 0.58) or multisensor data (cross-validated RMSE of 4.86 m and R2 of 0.60). Although the multisensor model presented the best performance, it was not statistically different from the single-S2 model. Thus, the use of only S2 to estimate canopy height has practical advantages, as it reduces the need to process SAR images and the uncertainties due to S1 noise or differences between the acquisition dates of S2 and S1. [ABSTRACT FROM AUTHOR]

Forest aboveground biomass (AGB) is an important indicator for characterizing forest ecosystem structures and functions. Therefore, how to effectively investigate forest AGB is a vital mission. Airborne laser scanning (ALS) has been demonstrated as an effective way to support investigation and operational applications among a wide range of applications in the forest inventory. Moreover, three-dimensional structure information relating to AGB can be acquired by airborne laser scanning. Many studies estimated AGB from variables that were extracted from point cloud data, but few of them took full advantage of variables related to tree crowns to estimate the AGB. In this study, the main objective was to evaluate and compare the capabilities of different metrics derived from point clouds obtained from ALS. Particularly, individual tree-based alpha-shape, along with other traditional and commonly used plot-level height and intensity metrics, have been used from airborne laser scanning data. We took the random forest and multiple stepwise linear regression to estimate the AGB. By comparing AGB estimates with field measurements, our results showed that the best approach is mixed metrics, and the best estimation model is random forest (R2 = 0.713, RMSE = 21.064 t/ha, MAE = 15.445 t/ha), which indicates that alpha-shape may be a good alternative method to improve AGB estimation accuracy. This method provides an effective solution for estimating aboveground biomass from airborne laser scanning. [ABSTRACT FROM AUTHOR]

Airborne Laser Scanning (ALS) is widely extended in forest evaluation, although photogrammetry-based Structure from Motion (SfM) has recently emerged as a more affordable alternative. Return cloud metrics and their normalization using different typologies of Digital Terrain Models (DTM), either derived from SfM or from private or free access ALS, were evaluated. In addition, the influence of the return density (0.5–6.5 returns m-2) and the sampling intensity (0.3–3.4%) on the estimation of the most common stand structure variables were also analysed. The objective of this research is to gather all these questions in the same document, so that they serve as support for the planning of forest management. This study analyses the variables collected from 60 regularly distributed circular plots (r = 18 m) in a 150-ha of uneven-aged Scots pine stand. Results indicated that both ALS and SfM can be equally used to reduce the sampling error in the field inventories, but they showed differences when estimating the stand structure variables. ALS produced significantly better estimations than the SfM metrics for all the variables of interest, as well as the ALS-based normalization. However, the SfM point cloud produced better estimations when it was normalized with its own DTM, except for the dominant height. The return density did not have significant influence on the estimation of the stand structure variables in the range studied, while higher sampling intensities decreased the estimation errors. Nevertheless, these were stabilized at certain intensities depending on the variance of the stand structure variable. [ABSTRACT FROM AUTHOR]

Airborne laser scanning (ALS) is increasingly used for detailed vegetation structure mapping; however, there are many local-scale applications where it is economically ineffective or unfeasible from the temporal perspective. Unmanned aerial vehicles (UAVs) or airborne imagery (AImg) appear to be promising alternatives, but only a few studies have examined this assumption outside economically exploited areas (forests, orchards, etc.). The main aim of this study was to compare the usability of normalized digital surface models (nDSMs) photogrammetrically derived from UAV-borne and airborne imagery to those derived from low- (1–2 pts/m2) and high-density (ca. 20 pts/m2) ALS-scanning for the precise local-scale modelling of woody vegetation structures (the number and height of trees/shrubs) across six dynamically changing shrubland sites. The success of the detection of woody plant tops was initially almost 100% for UAV-based models; however, deeper analysis revealed that this was due to the fact that omission and commission errors were approximately equal and the real accuracy was approx. 70% for UAV-based models compared to 95.8% for the high-density ALS model. The percentage mean absolute errors (%MAE) of shrub/tree heights derived from UAV data ranged between 12.2 and 23.7%, and AImg height accuracy was relatively lower (%MAE: 21.4–47.4). Combining UAV-borne or AImg-based digital surface models (DSM) with ALS-based digital terrain models (DTMs) significantly improved the nDSM height accuracy (%MAE: 9.4–13.5 and 12.2–25.0, respectively) but failed to significantly improve the detection of the number of individual shrubs/trees. The height accuracy and detection success using low- or high-density ALS did not differ. Therefore, we conclude that UAV-borne imagery has the potential to replace custom ALS in specific local-scale applications, especially at dynamically changing sites where repeated ALS is costly, and the combination of such data with (albeit outdated and sparse) ALS-based digital terrain models can further improve the success of the use of such data. [ABSTRACT FROM AUTHOR]

Individual tree detection algorithms (ITD) are used to obtain accurate information about trees. Following the process of individual tree detection, it is possible to use additional processing tools to determine tree parameters such as tree height, crown base height, crown volume, or stem volume. However, many of the methods developed so far have focused on parameterising the algorithms based on the study area, height structure or tree species analysed. Applying the parameters of the method can be challenging in areas with dense and heterogeneous forests with a diverse stand structure. Therefore, this work aimed to develop a method to correct the results of ITD algorithms to identify individual trees more reliably, taking into account different ITD methods based on the Canopy Height Model. In the present study, we proposed a three-step approach to correct segmentation errors. In the first step, erroneous (under- and over-segmentation errors) and correct segments were classified. After classification, the second step was to refine the under-segmentation errors. The final step was to merge segments from the over-segmentation class with correct segments based on the specified conditions. The study was conducted in one of the most complex and diverse forest communities in Europe, making tree identification a major challenge. The accuracy of the segmentation improvements varied depending on the method applied and tree species group examined. Thus, based on the results, the paper advocates for the correction method due to its efficiency in mixed forest stands. Therefore, the present study offers a possible solution to reduce segmentation errors by considering different forest types and different CHM-based ITD methods for identifying individual trees. [ABSTRACT FROM AUTHOR]

Automatic tree crown segmentation from remote sensing data is especially challenging in dense, diverse, and multilayered tropical forest canopies, and tracking mortality by this approach is even more difficult. Here, we examine the potential for combining airborne laser scanning (ALS) with multispectral and hyperspectral data to improve the accuracy of tree crown segmentation at a study site in French Guiana. We combined an ALS point cloud clustering method with a spectral deep learning model to achieve 83% accuracy at recognizing manually segmented reference crowns (with congruence >0.5). This method outperformed a two-step process that involved clustering the ALS point cloud and then using the logistic regression of hyperspectral distances to correct oversegmentation. We used this approach to map tree mortality from repeat surveys and show that the number of crowns identified in the first that intersected with height loss clusters was a good estimator of the number of dead trees in these areas. Our results demonstrate that multisensor data fusion improves the automatic segmentation of individual tree crowns and presents a promising avenue to study forest demography with repeated remote sensing acquisitions.

The paper investigates the usage of LiDAR (light detection and ranging) data for the automation of mapping vegetation with respect to the evaluation of the ecological succession process. The study was performed for the repository of the “Fryderyk” mine (southern Poland). The post-flotation area analyzed is a unique refuge habitat—Natura2000, PLH240008—where a forest succession has occurred for several dozen years. Airborne laser scanning (ALS) point clouds were used for deriving detailed information about the morphometry of the spoil heap and about the secondary forest succession process—mainly vegetation parameters i.e., height and canopy cover. The area of the spoil heap is irregular with a flat top and steep slopes above 20°. Analyses of ALS point clouds (2011 and 2019), confirmed progression in the forest succession process, and land cover changes especially in wooded or bushed areas. Precise vegetation parameters (3D LiDAR metrics) were calculated and provided the following parameters: mean value of vegetation height as 6.84 m (2011) and 8.41 m (2019), and canopy cover as 30.0% (2011) and 42.0% (2019). Changes in vegetation volume (3D area) were shown: 2011—310,558 m3, 2019—325,266 m3, vegetation removal—85,136 m3, increasing ecological succession—99,880 m3.

This research employs machine learning (Mask Region-Based Convolutional Neural Networks [Mask R-CNN]) and cluster analysis (Density-based spatial clustering of applications with noise [DBSCAN]) to identify more than 20,000 relict charcoal hearths (RCHs) organized in large "fields" within and around State Game Lands (SGLs) in Pennsylvania. This research has two important threads that we hope will advance the archaeological study of landscapes. The first is the significant historical impact of charcoal production, a poorly understood industry of the late eighteenth to early twentieth century, on the historic and present landscape of the United States. Although this research focuses on charcoal production in Pennsylvania, it has broad application for both identifying and contextualizing historical charcoal production throughout the world and for better understanding modern charcoal production. The second thread is the use of open data, open source, and open access tools to conduct this analysis, as well as the open publication of the resultant data. Not only does this research demonstrate the significance of open access tools and data but the open publication of our code as well as our data allow others to replicate our work, to tweak our code and protocols for their own work, and reuse our results. [ABSTRACT FROM AUTHOR]

Accurate highway alignments and three-dimensional (3D) models are essential for various intelligent transportation applications. Airborne laser scanning (ALS) provides a desirable means of data collection, which increases data quality and collection efficiency. However, automatic alignments extraction and 3D modeling remain open problems. Therefore, this paper proposes an effective framework to extract highway alignments by minimizing an elaborate energy function and reconstruct highway 3D models with the restrictions of alignments. Specifically, the proposed method contains the following steps: (1) Adopt an adaptive method based on spatially smooth and interconnected grid cells to recognize highway pavement points from ALS data. (2) Extract pavement boundaries and lane markings from the pavement areas using the α-shape algorithm and a marking tracking strategy. (3) Extract highway alignments by minimizing an energy function and reconstruct highway 3D models with the restrictions of alignments. The method was validated in scenes of various highways, where the point density is 10–25 pts/m2. The extracted alignments respectively achieved the correctness of 90.67% and 99.25% and the completeness of 87.60% and 99.55% within 10 cm and 15 cm errors. The root mean square error (RMSE) of the generated 3D model is 2.4 cm on pavement and 5.8 cm on hills and slopes.

Forests play a central role in the management of the Earth’s climate. Airborne laser scanning (ALS) technologies facilitate the monitoring of large and impassable areas and can be used to monitor the 3D structure of forests. While the ALS-based forest measures have been studied in depth, 3D change detection in forests is still a subject of little attention in the literature due to the challenges introduced by comparing point cloud pairs. In this study, we propose an innovative methodology to (i) automatically perform a 3D change detection of forests on an individual tree level; (ii) estimate tree parameters with allometric equations; and (iii) perform an assessment of the aboveground biomass (AGB) variation over time. The area in which the tests were carried out was hit by an ice storm that occurred in the time interval between the two LiDAR acquisitions; furthermore, field measurements were carried out and used to validate the results. The single-tree segmentation of the point clouds was automatically performed with a local maxima algorithm to detect the treetop, and a decision tree method to define the individual crowns around the local maxima. The multitemporal comparison of the point clouds was based on the identification of single trees, which were matched when there was a correlation between the position of the treetops. For each tree, the DBH (diameter at breast height) and the AGB were also estimated using allometric equations. The results are promising and allowed us to identify the uprooted trees and estimate that about 40% of the AGB of the area under examination had been destroyed, with an RMSE over the estimation ranging between 4% and 21% in four scenarios.

Digital aerial photogrammetry (DAP) has emerged as an alternative to airborne laser scanning (ALS) for forest inventory applications, as it offers a low-cost and flexible three-dimensional (3D) point cloud. Unlike the forest inventory attributes (e.g., tree height and diameter at breast height), the relative ability of DAP and ALS in predicting canopy structural variables (i.e., canopy cover and leaf area index (LAI)) has not been sufficiently investigated by previous studies. In this study, we comprehensively compared the canopy cover and LAI estimates using DAP- and ALS-based methods over 166 selected tropical forest sample plots with seven different tree species and forest types. We also explored the relationship between field-measured aboveground biomass (AGB) and the LAI estimates. The airborne LAI estimates were subsequently compared with the Sentinel-2-based LAI values that were retrieved using a one-dimensional radiative transfer model. The results demonstrated that the DAP-based method generally overestimated the two canopy variables compared to ALS-based methods but with relatively high correlations regardless of forest type and species (R2 of 0.80 for canopy cover and R2 of 0.76 for LAI). Under different forest types and species, the R2 of canopy cover and LAI range from 0.64 to 0.89 and from 0.54 to 0.87, respectively. Apparently, different correlations between AGB and LAI were found for different forest types and species where the mixed coniferous and broad-leaved forest shows the best correlation with R2 larger than 0.70 for both methods. The comparison with satellite retrievals verified that the ALS-based estimates are more consistent with Sentinel-2-based estimates than DAP-based estimates. We concluded that DAP data failed to provide analogous results to ALS data for canopy variable estimation in tropical forests.

Accurate estimation of above-ground biomass (AGB) in forested areas is essential for studying forest ecological functions, surface carbon cycling, and global carbon balance. Over the past decade, models that harness the distinct features of multi-source remote sensing observations for estimating AGB have gained significant popularity. It is worth exploring the differences in model performance by using simple and fused data. Additionally, quantitative estimation of the impact of high-cost laser point clouds on satellite imagery of varying costs remains largely unexplored. To address these challenges, model performance and cost must be considered comprehensively. We propose a comprehensive assessment based on three perspectives (i.e., performance, potential and limitations) for four typical AGB-estimation models. First, different variables are extracted from the multi-source and multi-resolution data. Subsequently, the performance of four regression methods is tested for AGB estimation with diverse indicator combinations. Experimental results prove that the combination of multi-source data provides a highly accurate AGB regression model. The proposed regression and variables rating approaches can flexibly integrate other data sources for modeling. Furthermore, the data cost is discussed against the AGB model performance. Our study demonstrates the potential of using low-cost satellite data to provide a rough AGB estimation for larger areas, which can allow different remote sensing data to meet different needs of forest management decisions. [ABSTRACT FROM AUTHOR]

Knowledge about the aboveground biomass (AGB) and the diameters at breast height (DBH) distribution can lead to a precise estimation of carbon density and forest structure which can be very important for ecology studies especially for those concerning climate change. In this study, we propose to predict DBH and AGB of individual trees using tree height (H) and crown diameter (CD), and other metrics extracted from airborne laser scanning (ALS) data as input. In the proposed approach, regression methods, such us support vector machine for regression (SVR) and random forests (RF), were used to find a transformation or a transfer function that links the input parameters (H, CD, and other ALS metrics) with the output (DBH and AGB). The developed approach was tested on two datasets collected in southern Norway comprising 3970 and 9467 recorded trees, respectively. The results demonstrate that the developed approach provides better results compared to a state-of-the-art work (based on a linear model with the standard least-squares method) with RMSE equal to 81.4 kg and 92.0 kg, respectively (compared to 94.2 kg and 110.0 kg) for the prediction of AGB, and 5.16 cm and 4.93 cm, respectively (compared to 5.49 cm and 5.30 cm) for DBH.

Continuous mapping of vegetation height is critical for many forestry applications, such as planning vegetation management in power transmission line right-of-way. Satellite images from different sensors, including SAR (Synthetic Aperture Radar) from Sentinel 1 (S1) and multispectral from Sentinel 2 (S2), can be used for producing high-resolution vegetation height maps at a broad scale. The main objective of this study is to assess the potential of S1 and S2 satellite data, both in a single and a multisensor approach, for modeling canopy height in a transmission line right-of-way located in the Atlantic Forest of Paraná, Brazil. For integrating S1 and S2 data, we used three machine learning algorithms (LR: Linear Regression, CART: Classification and Regression Trees, and RF: Random Forest) and airborne LiDAR (Light Detection and Ranging) measurements as the reference height. The best models were obtained using the RF algorithm and 20 m resolution features from only S2 data (cross-validated RMSE of 4.92 m and R2 of 0.58) or multisensor data (cross-validated RMSE of 4.86 m and R2 of 0.60). Although the multisensor model presented the best performance, it was not statistically different from the single-S2 model. Thus, the use of only S2 to estimate canopy height has practical advantages, as it reduces the need to process SAR images and the uncertainties due to S1 noise or differences between the acquisition dates of S2 and S1.

Forest aboveground biomass (AGB) is an important indicator for characterizing forest ecosystem structures and functions. Therefore, how to effectively investigate forest AGB is a vital mission. Airborne laser scanning (ALS) has been demonstrated as an effective way to support investigation and operational applications among a wide range of applications in the forest inventory. Moreover, three-dimensional structure information relating to AGB can be acquired by airborne laser scanning. Many studies estimated AGB from variables that were extracted from point cloud data, but few of them took full advantage of variables related to tree crowns to estimate the AGB. In this study, the main objective was to evaluate and compare the capabilities of different metrics derived from point clouds obtained from ALS. Particularly, individual tree-based alpha-shape, along with other traditional and commonly used plot-level height and intensity metrics, have been used from airborne laser scanning data. We took the random forest and multiple stepwise linear regression to estimate the AGB. By comparing AGB estimates with field measurements, our results showed that the best approach is mixed metrics, and the best estimation model is random forest (R2 = 0.713, RMSE = 21.064 t/ha, MAE = 15.445 t/ha), which indicates that alpha-shape may be a good alternative method to improve AGB estimation accuracy. This method provides an effective solution for estimating aboveground biomass from airborne laser scanning.

Crown features derived from high-density airborne laser scanning (ALS) data have proven to be effective for forest species classification at the individual tree level. Most of the general state-of-the-art (SoA) techniques rely on coarse-level crown features extracted from ALS data and under-utilize both the spatial and the spectral information available in the point clouds, Moreover, they are designed on the expected properties of the specific analyzed forest. We present a novel species classification approach, based on quantization of the entire 3-D tree crown into smaller elementary crown volumes (ECVs) that effectively captures the spatial distribution of filled (i.e., stem, branch, and foliage) and empty volumes of crowns. In the first step, a data-driven process dynamically tests and compares three quantization strategies to tailor the definition of the ECV to the forest type (e.g., conifer and deciduous forest). In the second step, for each ECV, a histogram vector is made up of features representing the light detection and ranging (LiDAR) point distribution and intensity to model the internal and the external local crown characteristics. Then, tree histogram feature vectors are obtained by stacking all the ECV histogram feature vectors. Finally, classification is performed by a support vector machine (SVM) classifier using the histogram intersection kernel. All experiments were performed on three high-density (50–200 points/m2) ALS data sets of deciduous, conifer, and mixed (i.e., both deciduous and conifer) trees. The higher classification accuracy of the proposed method over the SoA one proves its ability to better capture the crown characteristics of individual trees, including species-specific traits. [ABSTRACT FROM AUTHOR]