Search

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.

Filtering from airborne LiDAR datasets in urban area is one important process during the building of digital and smart cities. However, the existing filters encounter poor filtering performance and heavy computational burden when processing large-scale and complicated urban environments. To tackle this issue, a self-adaptive filtering method based on object primitive global energy minimization is proposed in this paper. In this paper, mode points were first acquired for generating the mode graph. The mode points were the cluster centers of the LiDAR data obtained in a mean shift algorithm. The graph constructed with mode points was named "mode graph" in this paper. By defining the energy function based on the mode graph, the filtering process is transformed to iterative global energy minimization. In each iteration, the graph cuts technique was adopted to achieve global energy minimization. Meanwhile, the probability of each point belonging to the ground was updated, which would lead to a new refined ground surface using the points whose probabilities were greater than 0.5. This process was iterated until two successive fitted ground surfaces were determined to be close enough. Four urban samples with different urban environments were adopted for verifying the effectiveness of the filter developed in this paper. Experimental results indicate that the developed filter obtained the best filtering performance. Both the total error and the Kappa coefficient are superior to those of the other three classical filtering methods. [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]

Airborne light detection and ranging (LiDAR) data are increasingly used in various fields such as topographic mapping, urban planning, and emergency management. A necessary processing step in the application of airborne LiDAR data is the elimination of mismatch errors. This paper proposes a new method for airborne LiDAR strip adjustment based on point clouds with planar neighborhoods; this method is intended to eliminate errors in airborne LiDAR point clouds. Initially, standard pre-processing tasks such as denoising, ground separation, and resampling are performed on the airborne LiDAR point clouds. Subsequently, this paper introduces a unique approach to extract point clouds with planar neighborhoods which is designed to enhance the registration accuracy of the iterative closest point (ICP) algorithm within the context of airborne LiDAR point clouds. Following the registration of the point clouds using the ICP algorithm, tie points are extracted via a point-to-plane projection method. Finally, a strip adjustment calculation is executed using the extracted tie points, in accordance with the strip adjustment equation for airborne LiDAR point clouds that was derived in this study. Three sets of airborne LiDAR point cloud data were utilized in the experiment outlined in this paper. The results indicate that the proposed strip adjustment method can effectively eliminate mismatch errors in airborne LiDAR point clouds, achieving a registration accuracy and absolute accuracy of 0.05 m. Furthermore, this method's processing efficiency was more than five times higher than that of traditional methods such as ICP and LS3D. [ABSTRACT FROM AUTHOR]

The interest in the enhancement of innovative solutions in the geospatial data classification domain from integrated aerial methods is rapidly growing. The transition from unstructured to structured information is essential to set up and arrange geodatabases and cognitive systems such as digital twins capable of monitoring territorial, urban, and general conditions of natural and/or anthropized space, predicting future developments, and considering risk prevention. This research is based on the study of classification methods and the consequent segmentation of low-altitude airborne LiDAR data in highly forested areas. In particular, the proposed approaches investigate integrating unsupervised classification methods and supervised Neural Network strategies, starting from unstructured point-based data formats. Furthermore, the research adopts Machine Learning classification methods for geo-morphological analyses derived from DTM datasets. This paper also discusses the results from a comparative perspective, suggesting possible generalization capabilities concerning the case study investigated. [ABSTRACT FROM AUTHOR]

In point-cloud scenes, semantic segmentation is the basis for achieving an understanding of a 3D scene. The disorderly and irregular nature of 3D point clouds makes it impossible for traditional convolutional neural networks to be applied directly, and most deep learning point-cloud models often suffer from an inadequate utilization of spatial information and of other related point-cloud features. Therefore, to facilitate the capture of spatial point neighborhood information and obtain better performance in point-cloud analysis for point-cloud semantic segmentation, a multiscale, multi-feature PointNet (MSMF-PointNet) deep learning point-cloud model is proposed in this paper. MSMF-PointNet is based on the classical point-cloud model PointNet, and two small feature-extraction networks called Mini-PointNets are added to operate in parallel with the modified PointNet; these additional networks extract multiscale, multi-neighborhood features for classification. In this paper, we use the spherical neighborhood method to obtain the local neighborhood features of the point cloud, and then we adjust the radius of the spherical neighborhood to obtain the multiscale point-cloud features. The obtained multiscale neighborhood feature point set is used as the input of the network. In this paper, a cross-sectional comparison analysis is conducted on the Vaihingen urban test dataset from the single-scale and single-feature perspectives. [ABSTRACT FROM AUTHOR]

Airborne LiDAR (Light Detection and Ranging) is an active Earth observing system, which can directly acquire high-accuracy and dense building roof data. Thus, airborne LiDAR has become one of the mainstream source data for building detection and reconstruction. The emphasis for building reconstruction focuses on the accurate extraction of feature lines. Building roof feature lines generally include the internal and external feature lines. Efficient extraction of these feature lines can provide reliable and accurate information for constructing three-dimensional building models. Most related algorithms adopt intersecting the extracted planes fitted by the corresponding points. However, in these methods, the accuracy of feature lines mostly depends on the results of plane extraction. With the development of airborne LiDAR hardware, the point density is enough for accurate extraction of roof feature lines. Thus, after acquiring the results of building detection, this paper proposed a feature lines extraction strategy based on the geometric characteristics of the original airborne LiDAR data, tracking roof outlines, normal ridge lines, oblique ridge lines and valley lines successively. The final refined feature lines can be obtained by normalization. The experimental results showed that our methods can achieve several promising and reliable results with an accuracy of 0.291 m in the X direction, 0.295 m in the Y direction and 0.091 m in the H direction for outlines extraction. Further, the internal feature lines can be extracted with reliable visual effects using our method. [ABSTRACT FROM AUTHOR]

Since the shorelines are important geographical boundaries, monitoring shoreline change plays an important role in integrated coastal management. With the evolution of remote sensing technology, many studies have used optical images to measure and to extract shoreline. However, some factors limit the use of optical imaging on shoreline mapping. Considering that the airborne LiDAR data can provide more accurate topographical information, there are some studies that have been investigated using airborne LiDAR to map shorelines. However, a literature review that combines airborne LiDAR with shoreline measurement and extracting methods has not yet been conducted. The motivation of this paper is to present a narrative review of shoreline mapping by using airborne LiDAR, including a laser scanning system, data availability, and current extraction techniques over the past two decades. Therefore, we conducted a broad search and finally summarized more than 130 articles on airborne LiDAR technology for shoreline measurement and shoreline extraction. We find that shoreline mapping by using airborne LiDAR still meets the challenge, such as objective condition limitations, data availability limitations, and self-characteristic limitations. The current method of shoreline extraction has a great potential to be improved; particularly when combined with the emerging current state-of-the-art LiDAR point cloud processing techniques (e.g., deep-learning algorithms), they will have a brighter future. This review paper provides an overview and the current trend of shoreline mapping using airborne LiDAR, and points out the limitations, challenges, and future opportunities. Moreover, it also can serve as a starting point for novices and experts to study the shoreline mapping by using airborne LiDAR, which provides a scientific support for studying shoreline changes. [ABSTRACT FROM AUTHOR]

Measuring three-dimensional (3D) building expansion and quantifying both horizontal and vertical dimensions in a nonautomated manner may not be possible for big data sets. This paper aims to present novel metrics for measuring both vertical and horizontal developments and to develop a web-based app and a dashboard workflow which identifies differences in the form and scale of buildings. The novel 3D metrics are developed based on the concepts of contact surface and 3D discrete compactness. A workflow was developed exploiting the potential of advanced geographic information system (GIS) functions, along with airborne lidar data processing, to apply the metrics on both a simulation of big data sets and digital building models from point clouds. The database system, including a geo-web-based app, is developed for visualizing 3D compactness computations on a dashboard and sharing the outcome with practitioners using their own smartphones. In the simulation experiment, nine 3D building block types were created, and the computation results were compared with the same size building blocks, which were derived from lidar point cloud data sets. The computation results of both the simulation and airborne lidar data, which included 123 and 127,407 data points, respectively, show that the novel metrics are able to identify differences among the typologies and intermediate 3D patterns. The presented workflow also enables online visualization of the computation output. [ABSTRACT FROM AUTHOR]

This paper presents an application of data-driven Dempster-Shafer theory (DST) of evidence to fuse multisensor data for land-cover feature extraction. Over the years, researchers have focused on DST for a variety of applications. However, less attention has been given to generate and interpret probability, certainty, and conflict maps. Moreover, quantitative assessment of DST performance is often overlooked. In this paper, for implementation of DST, two main types of data were used: multisensor data such as Light Detection and Ranging (LiDAR) and multispectral satellite imagery [Satellite Pour l'Observation de la Terre 5 (SPOT 5)]. The objectives are to classify land-cover types from fused multisensor data using DST, to quantitatively assess the accuracy of the classification, and to examine the potential of slope data derived from LiDAR for feature detection. First, we derived the normalized difference vegetation index (NDVI) from SPOT 5 image and the normalized digital surface model (DSM) (nDSM) from LiDAR by subtracting the digital terrain model from the DSM. The two products were fused using the DST algorithm, and the accuracy of the classification was assessed. Second, we generated a surface slope from LiDAR and fused it with NDVI. Subsequently, the classification accuracy was assessed using an IKONOS image of the study area as ground truth data. From the two processing stages, the NDVI/nDSM fusion had an overall accuracy of 88.7%, while the NDVI/slope fusion had 75.3%. The result indicates that NDVI/nDSM integration performed better than NDVI/slope. Although the overall accuracy of the former is better than the latter (NDVI/slope), the contribution of individual class reveals that building extraction from fused slope and NDVI performed poorly. This study proves that DST is a time- and cost-effective method for accurate land-cover feature identification and extraction without the need for a prior knowledge of the scene. Furthermore, the ability to generate other products like certainty, conflict, and maximum probability maps for better visual understanding of the decision process makes it more reliable for applications such as urban planning, forest management, 3-D feature extraction, and map updating. [ABSTRACT FROM PUBLISHER]

Forests are the main part of the terrestrial ecosystem. Airborne LiDAR is fast, comprehensive, penetrating, and contactless and can depict 3D canopy information with a high efficiency and accuracy. Therefore, it plays an important role in forest ecological protection, tree species recognition, carbon sink calculation, etc. Accurate recognition of individual trees in forests is a key step to various application. In real practice, however, the accuracy of individual tree segmentation (ITS) is often compromised by under-segmentation due to the diverse species, obstruction and understory trees typical of a high-density multistoried mixed forest area. Therefore, this paper proposes an ITS optimization method based on Gaussian mixture model for airborne LiDAR data. First, the mean shift (MS) algorithm is used for the initial ITS of the pre-processed airborne LiDAR data. Next, under-segmented samples are extracted by integrated learning, normally segmented samples are classified by morphological approximation, and the approximate distribution uncertainty of the normal samples is described with a covariance matrix. Finally, the class composition among the under-segmented samples is determined, and the under-segmented samples are re-segmented using Gaussian mixture model (GMM) clustering, in light of the optimal covariance matrix of the corresponding categories. Experiments with two datasets, Trento and Qingdao, resulted in ITS recall of 94% and 96%, accuracy of 82% and 91%, and F-scores of 0.87 and 0.93. Compared with the MS algorithm, our method is more accurate and less likely to under-segment individual trees in many cases. It can provide data support for the management and conservation of high-density multistoried mixed forest areas. [ABSTRACT FROM AUTHOR]

This paper presents an advanced method for the reliable detection of failures and online calibration of airborne LiDARs and cameras in photogrammetric mapping of remote sensing imagery capture scenarios. Traditional calibration methods without targets typically depend on aligning the spatial structures of LiDAR data with texture features, but acquiring dense LiDAR data for matching and optimization in a short amount of time is challenging in real-time operations. In response to this issue, this paper proposes an image feature encoding method designed to dynamically guide the matching of LiDAR feature points, while simultaneously employing context consistency estimation for additional optimization. Importantly, our method can perceive and recalibrate extrinsic errors in real-time scenarios within natural environments, thereby enhancing the robustness of the calibration. The effectiveness and adaptability of our method are demonstrated using our unique airborne LiDAR-based LIVOX-Airborne datasets, which set a new benchmark in both numerical variability and pixel discrepancy, showcasing a variance of 5.05×10−4 and a mean error of 0.86 pixels.

The safety of the electricity infrastructure significantly affects both our daily life and industrial activities. Timely and accurate monitoring of the safety of electricity network can prevent dangerous situations effectively. Thus, we, in this paper, develop a voxel-based method for automatically extracting the transmission lines from airborne LiDAR point cloud data. The method proposed in this paper uses three-dimensional (3-D) voxels as primitives and consist of the following steps: First, skeleton structure extraction using Laplacian smoothing; second, feature construction of a 3-D voxel using Latent Dirichlet allocation topic model; and third Markov random field model-based extraction for generating locally continuous and globally optimal results. To evaluate the effectiveness and robustness of the proposed method, experiments were conducted on four different types of power line scenes with flat and complex terrains from helicopter-borne LiDAR point cloud data. Experimental results demonstrate that our proposed method is efficient and robust for automatically detecting both the single conductor and the bundled conductors, with precision, recall, and quality of over 96.78%, 98.67%, and 96.66%, respectively. Moreover, compared with other existing methods, our proposed method provides higher detection correctness rate. [ABSTRACT FROM AUTHOR]

Filtering is a key step for most applications of airborne LiDAR point clouds. Although lots of filtering algorithms have been put forward in recent years, most of them suffer from parameters setting or thresholds adjusting, which will be time-consuming and reduce the degree of automation of the algorithm. To overcome this problem, this paper proposed a threshold-free filtering algorithm based on expectation-maximization. The proposed algorithm is developed based on an assumption that point clouds are seen as a mixture of Gaussian models. The separation of ground points and non-ground points from point clouds can be replaced as a separation of a mixed Gaussian model. Expectation-maximization (EM) is applied for realizing the separation. EM is used to calculate maximum likelihood estimates of the mixture parameters. Using the estimated parameters, the likelihoods of each point belonging to ground or object can be computed. After several iterations, point clouds can be labelled as the component with a larger likelihood. Furthermore, intensity information was also utilized to optimize the filtering results acquired using the EM method. The proposed algorithm was tested using two different datasets used in practice. Experimental results showed that the proposed method can filter non-ground points effectively. To quantitatively evaluate the proposed method, this paper adopted the dataset provided by the ISPRS for the test. The proposed algorithm can obtain a 4.48% total error which is much lower than most of the eight classical filtering algorithms reported by the ISPRS. [ABSTRACT FROM AUTHOR]

Automatically generating a building footprint from an airborne LiDAR point cloud is an active research topic because of its widespread usage in numerous applications. This paper presents an efficient and automated workflow for generating building footprints from pre-classified LiDAR data. In this workflow, LiDAR points that belong to the building category are first segmented into multiple clusters by applying the grid-based DBSCAN clustering algorithm. Each cluster contains the points of an individual building. Then, the outermost points of each building are extracted, on which the recursive convex hull algorithm is applied to generate the initial outline of each building. Since LiDAR points are irregularly distributed, the initial building outline contains irregular zig-zag shapes. In order to achieve a regularized building footprint that is close to the true building boundary, a signal-based regularization algorithm is developed. The initial outline is first transformed into a signal, which can reveal the wholistic geometric structure of the building outline after applying a denoising procedure. By analyzing the denoised signal, the locations of corners are identified, and the regularized building footprint is generated. The performance of the proposed workflow is tested and evaluated using two datasets that have different point densities and building types. The qualitative assessment reveals that the proposed workflow has a satisfying performance in generating building footprints even for building with complex structures. The quantitative assessment compares the performance of signal-based regularization with existing regularization methods using the 149 buildings contained in the test dataset. The experimental result shows the proposed method has achieved superior results based on a number of commonly used accuracy metrics. [ABSTRACT FROM AUTHOR]

Lidar data have been regarded as the most important data source for accurate forest biomass estimation. Different platforms such as terrestrial Laser scanning, unmanned aerial vehicle Laser scanning, airborne Laser scanning, and spaceborne Lidar (e.g. ICESat-1/2, GEDI, GF-7 Lidar) provide new opportunities to map forest biomass distribution at different scales. The ground-based Lidar data are mainly used for extracting individual tree parameters such as diameter at breast height (DBH) and tree height, attempting to replace or reduce field work, while spaceborne Lidar data are often used to extract canopy height data at national and global scales, but cannot provide wall-to-wall mapping. The airborne Lidar may be the most frequently used data for forest biomass estimation at local scale. Many studies have been conducted for mapping forest biomass distributions in different climate zones, but current research situations and challenges of using airborne Lidar data have not been fully overviewed. This paper attempts to provide an overview of using airborne Lidar data for forest biomass estimation and discuss current research problems and future directions, which will be valuable for professionals and practitioners to better understand the important role of using airborne Lidar data for forest biomass estimation at the local scale. [ABSTRACT FROM AUTHOR]

Individual tree detection (ITD) and crown delineation are two of the most relevant methods for extracting detailed and reliable forest information from LiDAR (Light Detection and Ranging) datasets. However, advanced computational skills and specialized knowledge have been normally required to extract forest information from LiDAR.The development of accessible tools for 3D forest characterization can facilitate rapid assessment by stakeholders lacking a remote sensing background, thus fostering the practical use of LiDAR datasets in forest ecology and conservation. This paper introduces the treetop application, an open‐source web‐based and R package LiDAR analysis tool for extracting forest structural information at the tree level, including cutting‐edge analyses of properties related to forest ecology and management.We provide case studies of how treetop can be used for different ecological applications, within various forest ecosystems. Specifically, treetop was employed to assess post‐hurricane disturbance in natural temperate forests, forest homogeneity in industrial forest plantations and the spatial distribution of individual trees in a tropical forest.treetop simplifies the extraction of relevant forest information for forest ecologists and conservationists who may use the tool to easily visualize tree positions and sizes, conduct complex analyses and download results including individual tree lists and figures summarizing forest structural properties. Through this open‐source approach, treetop can foster the practical use of LiDAR data among forest conservation and management stakeholders and help ecological researchers to further understand the relationships between forest structure and function. [ABSTRACT FROM AUTHOR]

When airborne lidar is used to produce digital elevation models, the random error of airborne lidar systems is often the limiting factor. To improve the overall 3-D expected accuracy of airborne lidar systems, we develop random error models of airborne lidar systems. First, we present a footprint geolocation equation for the airborne lidar that describes an ideal system, followed by a modified footprint geolocation equation that contains systematic and random errors. To better constrain these uncertainties, we describe the mathematical characteristics of the random error in detail by modeling it as a stochastic process. The probabilistic random error models are developed by analyzing the differences between the errorless equation and the equation with errors. This paper also addresses the issue of recovering the errors and presents an error recovery model. Finally, based on a linear scanner and a new coordinate system, we simulate the geometric processes of point positioning of airborne lidar systems and study the impacts of the random measurement errors on the 3-D coordinate accuracy of airborne lidar systems. [ABSTRACT FROM PUBLISHER]

Featured Application: DEM extraction from airborne LiDAR data. Most filtering algorithms suffer from complex parameter settings or threshold adjusting. To solve this problem, this paper proposes an improved skewness balancing filtering algorithm based on thin plate spline (TPS) interpolation. The proposed algorithm filters the nonground points in an iterative manner. A reference surface that reflects the fluctuation of the terrain is generated using the TPS interpolation method. Accordingly, the elevation difference from each point to the surface can be calculated. By applying the skewness balancing principle to these elevation differences, nonground points can be removed automatically. To verify the validity and robustness of the proposed method, the datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) were adopted. The experimental results show that this presented method can adapt to complex environments and achieve a higher filtering accuracy than the traditional skewness balancing algorithm. Moreover, in comparison with the other eight filtering methods tested by the ISPRS and four improved filtering methods proposed recently, the proposed method achieved an average total error of 5.39%, which is smaller than that of most of these other methods. [ABSTRACT FROM AUTHOR]

Mapping of trees plays an important role in modern urban spatial data management, as many benefits and applications inherit from this detailed up-to-date data sources. Timely and accurate acquisition of information on the condition of urban trees serves as a tool for decision makers to better appreciate urban ecosystems and their numerous values which are critical to building up strategies for sustainable development. The conventional techniques used for extracting trees include ground surveying and interpretation of the aerial photography. However, these techniques are associated with some constraints, such as labour intensive field work and a lot of financial requirement which can be overcome by means of integrated LiDAR and digital image datasets. Compared to predominant studies on trees extraction mainly in purely forested areas, this study concentrates on urban areas, which have a high structural complexity with a multitude of different objects. This paper presented a workflow about semi-automated approach for extracting urban trees from integrated processing of airborne based LiDAR point cloud and multispectral digital image datasets over Istanbul city of Turkey. The paper reveals that the integrated datasets is a suitable technology and viable source of information for urban trees management. As a conclusion, therefore, the extracted information provides a snapshot about location, composition and extent of trees in the study area useful to city planners and other decision makers in order to understand how much canopy cover exists, identify new planting, removal, or reforestation opportunities and what locations have the greatest need or potential to maximize benefits of return on investment. It can also help track trends or changes to the urban trees over time and inform future management decisions. [ABSTRACT FROM AUTHOR]

Ground filtering for airborne light detection and ranging (LiDAR) is one of the core steps to obtain digital terrain model (DTM) in complex terrain. In view of the deficiencies of excessive erosion as well as low automation in existing filtering method, this paper proposes a spatial clustering filtering (SCF) method. Firstly, the point cloud data is described as the Octree index structure, and then segmented based on plane fitting. After that, a coarse spatial clustering process based on dual distance is implemented to obtain a set of seed points. Secondly, a triangular irregular network (TIN) is built based on the classic progressive densification method with a set of seed points, and the rest of the valid point clouds are classified iteratively. Finally, the experimental results show that the SCF method in this paper can not only avoids the single threshold restrictions to the filtering result, but also maintains complicated topography when the ground information in the discontinuous terrain is filtered, all of the above are contribute to improve the reliability of the filtering result. [ABSTRACT FROM AUTHOR]

Automatic extraction of power lines has become a topic of great importance in airborne LiDAR data processing for transmission line management. In this paper, we present a new, fully automated and versatile framework that consists of four steps: (i) power line candidate point filtering, (ii) neighbourhood selection, (iii) feature extraction based on spatial topology, and (iv) SVM classification. In a detailed evaluation involving seven neighbourhood definitions, 26 geometric features and two datasets, we demonstrated that the use of multi-scale neighbourhoods for individual 3D points significantly improved the power line classification. Additionally, we showed that the spatial topological features may even further improve the results while reducing data processing time. [ABSTRACT FROM AUTHOR]

This paper presents a reliable and accurate method for building roof segmentation from airborne LiDAR data. In order to obtain the optimal results in both object level and pixel level, three energy minimization procedures are conducted consecutively in a hierarchical way. Firstly, an active multi-plane fitting method is conducted to obtain reliable initial segmentations. Then, the coarsest energy function composed of both the plane fitting errors in pixel level and the number of plane hypotheses in object level is minimized to obtain the optimal label space. Next, energy function composing of plane fitting errors and spatial smoothness between neighboring planes is minimized to obtain the optimal segmentation results. Finally, by taking prior knowledge of building roof structure into consideration, the optimal plane parameters for the segmented plane hypotheses are obtained by minimizing energy function of the structural adjusted plane fitting errors. Two real LiDAR data sets with different point densities and different building styles are used to evaluate the performance of the proposed method, and experimental results demonstrate that the proposed method is fast, stable, and reliable for accurate building roof segmentation from airborne LiDAR data. [ABSTRACT FROM AUTHOR]

This paper presents a new framework of road feature extraction from color component–based data fusion of aerial imagery and lidar data. The proposed framework consists of six procedures: (1) removal of elevated objects (e.g., buildings) from lidar data with a flatness index constraint; (2) removal of shadows and vegetation from aerial images using the Otsu segmentation; (3) data fusion of the modified lidar data and aerial images; (4) initial extraction of road features from the fused data; (5) refinement of road features to remove false positives and join up misclosures; and (6) final extraction of road surfaces and centerlines. A new method is proposed for data fusion of aerial images and lidar data to extract road features by utilizing color components, such as luminance, saturation, and hue, in hue/saturation/intensity and brightness/ blue difference/red difference color spaces. A series of refinement processes, including hierarchical median filtering and k-nearest-neighborhood, are implemented to remove open areas (e.g., parking lots) of the road extraction results. A local spatial interpolation method is applied to join up misclosures, and curve fitting is used to obtain accurate road centerlines. The results of tests on sample data sets indicate that the proposed framework performs well, with high accuracy, completeness, and quality. [ABSTRACT FROM AUTHOR]

This paper presents the results of a research aimed at investigating by airborne LiDAR the ancient landscape of a sector of central northern Istria (Croatia) from prehistory to Roman times. The area, approximately corresponding to the territory of Oprtalj/Portole, is located in a strategic position just north of the mid–Mirna/Quieto river, an important waterway connecting coastal Istria with the inlands since prehistory. The elaboration of high-definition LiDAR data compared with aerial and satellite images, historical cartography and field surveys allowed us to identify unreported prehistoric and Roman features and also get detailed topographic information about already known sites. Beside a few protohistoric hill forts discovered in the past, an open-air Neolithic or Copper Age site, a probable Bronze Age burial mound and three small protohistoric fortifications have been identified. These hill forts are clustered around a north-south valley, which represents a preferential access route from the larger Mirna valley to the karst plateau north of Oprtalj/Portole. In the same plateau, small Roman buildings associated with enclosures and probable artificial ponds have been interpreted as remains of farmsteads related to small-scale herding and agricultural activities. In the southern sector of the investigated area, consisting of marls, sandstones and fertile soils, large scatters of archaeological materials sometimes associated to outcropping features are probably the remains of larger structures characterized by a well-developed agricultural economy. Particularly interesting is the identification of fossil rectilinear land division features that overlap with the cardines of Istrian centuriation. Before our study, centuriation was identified only in the territories of Poreč/Parenzo and Pula/Pola, hence believed not to extend beyond the Mirna river. The new evidence revealed by LiDAR and satellite images, however, shows that a large part of central and northern Istria, between the Mirna and the Dragonja rivers, was divided by the same grid. Based on these considerations, the archaeological evidence here presented makes the Istrian peninsula one of the largest areas in the entire Roman world to have undergone a single planned land division survey. [ABSTRACT FROM AUTHOR]

In this paper a new method is proposed for filtering airborne light detection and ranging (LiDAR) point cloud data based on kernel density estimation (KDE). The point cloud data is divided into a number of blocks at different sizes step by step. In each block, the kernel probability density of the elevation values of all points is estimated, and a threshold value is selected for data filtering by referring the elevation value of the maximum probability density point. The points whose elevation values are lower than the threshold are classified as ground points. Because the method does not focus on the calculation of individual points, the computation complexity is greatly reduced. Experimental results show that the filtering method is valid and efficient for massive point cloud filtering. [ABSTRACT FROM AUTHOR]

Urban green spaces, particularly urban trees, play a key role in enhancing the liveability of cities. The availability of accurate and upto- date maps of tree canopy cover is important for sustainable development of urban green spaces. LiDAR point clouds are widely used for the mapping of buildings and trees, and several LiDAR point cloud classification techniques have been proposed for automatic mapping. However, the effectiveness of point cloud classification techniques for automated tree extraction from LiDAR data can be impacted to the point of failure by the complexity of tree canopy shapes in urban areas. Multispectral imagery, which provides complementary information to LiDAR data, can improve point cloud classification quality. This paper proposes a reliable method for the extraction of tree canopy cover from fused LiDAR point cloud and multispectral satellite imagery data. The proposed method initially associates each LiDAR point with spectral information from the co-registered satellite imagery data. It calculates the normalised difference vegetation index (NDVI) value for each LiDAR point and corrects tree points which have been misclassified as buildings. Then, region growing of tree points, taking the NDVI value into account, is applied. Finally, the LiDAR points classified as tree points are utilised to generate a canopy cover map. The performance of the proposed tree canopy cover mapping method is experimentally evaluated on a data set of airborne LiDAR and WorldView 2 imagery covering a suburb in Melbourne, Australia. [ABSTRACT FROM AUTHOR]

Accurate prediction of walking travel rates is central to wide-ranging applications, including modeling historical travel networks, simulating evacuation from hazards, evaluating military ground troop movements, and assessing risk to wildland firefighters. Most of the existing functions for estimating travel rates have focused on slope as the sole landscape impediment, while some have gone a step further in applying a limited set of multiplicative factors to account for broadly defined surface types (e.g., “on-path” vs. “off-path”). In this study, we introduce the Simulating Travel Rates In Diverse Environments (STRIDE) model, which accurately predicts travel rates using a suite of airborne lidar-derived metrics (slope, vegetation density, and surface roughness) that encompass a continuous spectrum of landscape structure. STRIDE enables the accurate prediction of both on- and off-path travel rates using a single function that can be applied across wide-ranging environmental settings. The model explained more than 80% of the variance in the mean travel rates from three separate field experiments, with an average predictive error less than 16%. We demonstrate the use of STRIDE to map least-cost paths, highlighting its propensity for selecting logically consistent routes and producing more accurate yet considerably greater total travel time estimates than a slope-only model. [ABSTRACT FROM AUTHOR]

Aboveground biomass (AGB) is a vital indicator for studying carbon sinks in forest ecosystems. Semiarid forests harbor substantial carbon storage but received little attention due to the high spatial–temporal heterogeneity that complicates the modeling of AGB in this environment. This study assessed the performance of different data sources (annual monthly time-series radar was Sentinel-1 [S1]; annual monthly time series optical was Sentinel-2 [S2]; and single-temporal airborne light detection and ranging [LiDAR]) and seven prediction approaches to map AGB in the semiarid forests on the border between Gansu and Qinghai Provinces in China. Five experiments were conducted using different data configurations from synthetic aperture radar backscatter, multispectral reflectance, LiDAR point cloud, and their derivatives (polarimetric combination indices, texture information, vegetation indices, biophysical features, and tree height- and canopy-related indices). The results showed that S2 acquired better prediction (coefficient of determination [R2]: 0.62–0.75; root mean square error [RMSE]: 30.08–38.83 Mg/ha) than S1 (R2: 0.24–0.45; RMSE: 47.36–56.51 Mg/ha). However, their integration further improved the results (R2: 0.65–0.78; RMSE: 28.68–35.92 Mg/ha). The addition of single-temporal LiDAR highlighted its structural importance in semiarid forests. The best mapping accuracy was achieved by XGBoost, with the metrics from the S2 and S1 time series and the LiDAR-based canopy height information being combined (R2: 0.87; RMSE: 21.63 Mg/ha; relative RMSE: 14.45%). Images obtained during the dry season were effective for AGB prediction. Tree-based models generally outperformed other models in semiarid forests. Sequential variable importance analysis indicated that the most important S1 metric to estimate AGB was the polarimetric combination indices sum, and the S2 metrics were associated with red-edge spectral regions. Meanwhile, the most important LiDAR metrics were related to height percentiles. Our methodology advocates for an economical, extensive, and precise AGB retrieval tailored for semiarid forests. [ABSTRACT FROM AUTHOR]

Airborne light detection and ranging (LiDAR) data are increasingly used in various fields such as topographic mapping, urban planning, and emergency management. A necessary processing step in the application of airborne LiDAR data is the elimination of mismatch errors. This paper proposes a new method for airborne LiDAR strip adjustment based on point clouds with planar neighborhoods; this method is intended to eliminate errors in airborne LiDAR point clouds. Initially, standard pre-processing tasks such as denoising, ground separation, and resampling are performed on the airborne LiDAR point clouds. Subsequently, this paper introduces a unique approach to extract point clouds with planar neighborhoods which is designed to enhance the registration accuracy of the iterative closest point (ICP) algorithm within the context of airborne LiDAR point clouds. Following the registration of the point clouds using the ICP algorithm, tie points are extracted via a point-to-plane projection method. Finally, a strip adjustment calculation is executed using the extracted tie points, in accordance with the strip adjustment equation for airborne LiDAR point clouds that was derived in this study. Three sets of airborne LiDAR point cloud data were utilized in the experiment outlined in this paper. The results indicate that the proposed strip adjustment method can effectively eliminate mismatch errors in airborne LiDAR point clouds, achieving a registration accuracy and absolute accuracy of 0.05 m. Furthermore, this method’s processing efficiency was more than five times higher than that of traditional methods such as ICP and LS3D.

In three-dimensional (3D) airborne light detection and ranging (LiDAR) point-cloud data acquisition, noise point clusters (such as cloud, birds and incomplete scanning ground points) and isolated points are usually generated in the scanning process. Detection and elimination of these noise points directly affect the subsequent processing efficiency of the LiDAR point clouds. In this paper, a noise detection method from airborne LiDAR data based on spatial hierarchical directional relationship and region growing algorithm is proposed. First, the original airborne LiDAR points are divided into regular 3D grids, and the maximum point density unit is searched adaptively to select the initial surface seed points for region growing algorithm. Then, the spherical neighborhood is constructed with the initial seed point as the center, and fourteen main growth directions are generated based on the 3D space topology. Second, candidate seed points in each main direction are determined by the distance threshold. Finally, all LiDAR points are iteratively executed using candidate seed points as new region growing seed points. This paper selects two mountain terrain scenes with different cloud contents as the study area, and the precision, recall rates and F1-score of the proposed method reach 99.8%, 100% and 99.3%, respectively. This method can detect point-cloud clusters and isolated points, thus simplifying the LiDAR point clouds, providing basic support for the subsequent accurate data processing and analysis.

Building upon the positive outcomes and evidence of dissemination across the community of the first Special Issue "Remote Sensing and Geosciences for Archaeology", the second edition of this Special Series of Geosciences dedicated to "Earth Observation, Remote Sensing and Geoscientific Ground Investigations for Archaeological and Heritage Research" collects a varied body of original scientific research contributions showcasing the technological, methodological, and interpretational advances that have been achieved in this field of archaeological and cultural heritage sciences over the last years. The fourteen papers, published after rigorous peer review, allowed the guest editor to make considerations on the capabilities, limitations, challenges, and perspectives of Earth observation (EO), remote sensing (RS), and geoscientific ground investigations with regard to: (1) archaeological prospection with high resolution satellite SAR and optical imagery; (2) high resolution documentation of archaeological features with drones; (3) archaeological mapping with LiDAR towards automation; (4) digital fieldwork using old and modern data; (5) field and archaeometric investigations to corroborate archaeological hypotheses; (6) new frontiers in archaeological research from space in contemporary Africa; and (7) education and capacity building in EO and RS for cultural heritage. [ABSTRACT FROM AUTHOR]

The urbanization in each country is developing rapidly, and the contradiction between urban population growth and construction land is increasingly prominent. Urban environmental quality has the characteristics of uneven spatial distribution and dynamic temporal distribution. How to survey the distribution of ground objects is a hotspot. In this paper, we used Airborne LiDAR System (ALS) to obtain high-precision and high-density three-dimensional information on objects, a modified PointNet network with multi-scale neighbourhood features is proposed to learn more contextual features. We tested the performance of proposed method on GML(B) dataset and the effectiveness is also demonstrated. Our proposed method has good performance on airborne Li-DAR point classification, which can be widely used for urban spatial environment analyzing and protecting. [ABSTRACT FROM AUTHOR]

Filtering from airborne LiDAR datasets in urban area is one important process during the building of digital and smart cities. However, the existing filters encounter poor filtering performance and heavy computational burden when processing large-scale and complicated urban environments. To tackle this issue, a self-adaptive filtering method based on object primitive global energy minimization is proposed in this paper. In this paper, mode points were first acquired for generating the mode graph. The mode points were the cluster centers of the LiDAR data obtained in a mean shift algorithm. The graph constructed with mode points was named “mode graph” in this paper. By defining the energy function based on the mode graph, the filtering process is transformed to iterative global energy minimization. In each iteration, the graph cuts technique was adopted to achieve global energy minimization. Meanwhile, the probability of each point belonging to the ground was updated, which would lead to a new refined ground surface using the points whose probabilities were greater than 0.5. This process was iterated until two successive fitted ground surfaces were determined to be close enough. Four urban samples with different urban environments were adopted for verifying the effectiveness of the filter developed in this paper. Experimental results indicate that the developed filter obtained the best filtering performance. Both the total error and the Kappa coefficient are superior to those of the other three classical filtering methods.

Forest aboveground biomass (AGB) provides valuable information about the carbon cycle, carbon sink monitoring, and understanding of climate change factors. Remote sensing data coupled with machine learning models have been increasingly used for forest AGB estimation over local and regional extents. Landsat series provide a 50-year data archive, which is a valuable source for historical mapping over large areas. As such, this paper proposed a machine learning-based workflow for historical AGB estimation and its change analysis from 2001 to 2019 for the New York State's forests using Landsat historical imagery, airborne LiDAR, and forest plot data. As the object-based image analysis (OBIA) is able to incorporate spectral, contextual, and textural features into the regression model, the proposed method utilizes an OBIA approach and a random forest (RF) regression model implemented on the Google Earth Engine (GEE) cloud computing platform. Results demonstrated that there is a considerable decrease of 983.79 × 106 Mg/ha in the AGB of deciduous forests from 2001 to 2006, followed by an increase of 618.28 × 106 Mg/ha from 2006 to 2011, continued with an increase of 229.12 × 106 Mg/ha of deciduous forests from 2011–2016. Finally, the results demonstrated a slight change in AGB from 2016 to 2019. The transferability of the proposed framework provides a practical solution for monitoring forests in other states or even on a national scale. [ABSTRACT FROM AUTHOR]

Extracting digital elevation models (DTMs) from LiDAR data under forest canopy is a challenging task. This is because the forest canopy tends to block a portion of the LiDAR pulses from reaching the ground, hence introducing gaps in the data. This paper presents an algorithm for DTM extraction from LiDAR data under forest canopy. The algorithm copes with the challenge of low data density by generating a series of coarse DTMs by using the few ground points available and using trend surfaces to interpolate missing elevation values in the vicinity of the available points. This process generates a cloud of ground points from which the final DTM is generated. The algorithm has been compared to two other algorithms proposed in the literature in three different test sites with varying degrees of difficulty. Results show that the algorithm presented in this paper is more tolerant to low data density compared to the other two algorithms. The results further show that with decreasing point density, the differences between the three algorithms dramatically increased from about 0.5m to over 10m. [ABSTRACT FROM AUTHOR]

Accurately simulating the spatiotemporal distribution of mountain snow water equivalent improves estimates of available meltwater and benefits the water resource management community. In this paper we present the first integration of lidar‐derived distributed snow depth data into a physics‐based snow model using direct insertion. Over four winter seasons (2013–2016) the National Aeronautics and Space Administration/Jet Propulsion Laboratory (NASA/JPL) Airborne Snow Observatory (ASO) performed near‐weekly lidar surveys throughout the snowmelt season to measure snow depth at high resolution over the Tuolumne River Basin above Hetch Hetchy Reservoir in the Sierra Nevada Mountains of California. The modeling component of the ASO program implements the iSnobal model to estimate snow density for converting measured depths to snow water equivalent and to provide temporally complete snow cover mass and thermal states between flights. Over the four years considered in this study, snow depths from 36 individual lidar flights were directly inserted into the model to provide updates of snow depth and distribution. Considering all updates to the model, the correlation between ASO depths and modeled depths with and without previous updates was on average r2 = 0.899 (root‐mean‐square error = 12.5 cm) and r2 = 0.162 (root‐mean‐square error = 41.5 cm), respectively. The precise definition of the snow depth distribution integrated with the iSnobal model demonstrates how the ASO program represents a new paradigm for the measurement and modeling of mountain snowpacks and reveals the potential benefits for managing water in the region. Plain Language Summary: In regions that depend primarily on snow to support life, water availability is becoming an increasingly important topic. National Aeronautics and Space Administration (NASA)'s Airborne Snow Observatory (ASO) is a new platform for estimating the amount of water stored in mountain snowpacks. Since 2013, the ASO has combined detailed measurements of snow depth from an aircraft with snowpack density estimates from a physics‐based snow model to provide predictions of total snow water equivalent stored in the Tuolumne River Basin in the California Sierra Nevada. This work describes the process of updating the snow model using the measured ASO snow depths through a direct insertion process. When the distribution of all the snow in the basin is known more accurately, the model results are improved. Key Points: This is the first near‐real time application of a model state variable being periodically updated using lidar‐derived snow depthsThe first update in each year is most crucial since it defines the spatial pattern of snow accumulation prior to peak snow water equivalentEnsuing updates remain important for setting the spatial distribution of spring storms and when the duration between flights is prolonged [ABSTRACT FROM AUTHOR]

This paper shows a method to register point clouds from images of UAV-mounted airborne cameras as well as airborne laser scanner data. The focus is a general technique which does rely neither on linear or planar structures nor on the point cloud density. Therefore, the proposed approach is also suitable for rural areas and water bodies captured via different sensor configurations. This approach is based on a regular 2.5D grid generated from the segmented ground points of the 3D point cloud. It is assumed that initial values for the registration are already estimated, e.g. by measured exterior orientation parameters with the UAV mounted GNSS and IMU. These initial parameters are finely tuned by minimizing the distances between the 3D points of a target point cloud to the generated grid of the source point cloud in an iteration process. To eliminate outliers (e.g., vegetation points) a threshold for the distances is defined dynamically at each iteration step, which filters ground points during the registration. The achieved accuracy of the registration is up to 0.4 m in translation and up to 0.3 degrees in rotation, by using a raster size of the DEM of 2 m. Considering the ground sampling distance of the airborne data which is up to 0.4 m between the scan lines, this result is comparable to the result achieved by an ICP algorithm, but the proposed approach does not rely on point densities and is therefore able to solve registrations where the ICP have difficulties. [ABSTRACT FROM AUTHOR]

The development of robust and accurate filtering approaches for automated extraction of digital terrain models (DTMs) from airborne Light Detection and Ranging (LiDAR) data continues to be a challenge. The problem is due to the nature of LiDAR point clouds, the complexity of scene components, and the intrinsic structure of the terrain itself. This paper proposes a novel approach for filtering LiDAR point clouds, which exploits the spatial structure of the terrain and the spatial coherence among the LiDAR points. Terrain points are progressively detected through energy minimization using graph cuts. The energy function and graph model encode both pointwise closeness and pairwise smoothness. The DTM is then extracted through progressive filtering via the graph cuts. The performance of the proposed method is investigated using two datasets with different point densities, terrain complexity, and land covers. The results show that the filter can effectively remove nonterrain points, leading to an accurately extracted DTM. The filter is also compared with other methods reported in the literature, the comparison demonstrating that the proposed method exhibits advantages in terms of performance. [ABSTRACT FROM AUTHOR]

To improve the effectiveness and generality of existing methods of high voltage (HV) transmission lines extraction, this paper proposes a novel automatic extraction method of HV transmission lines using airborne LIDAR point cloud data by incorporating the geography of transmission corridors and the distribution characteristics of airborne LIDAR point cloud data. The proposed method results in the separation of ground objects by using a differentiation height threshold segmentation algorithm based on subspace features, which divide long-distance space into several smalldistance space sets to improve the generality of the algorithm. A height density segmentation algorithm is used to locate transmission towers and extract point cloud data of HV transmission lines to improve the efficiency of the algorithm. Feasibility test cases show that the proposed method can automatically extract HV transmission lines from airborne LIDAR point cloud data in flat terrains, canyon terrains, and steep slope terrains in an efficient manner, with high accuracy and generality. [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]

Reconstructing three-dimensional (3D) models of power equipment plays an increasingly important role in advancing digital twin power grids. To reconstruct a high-precision model, it is crucial to accurately obtain the pylon type and its necessary parameter information before modeling. This study proposes an improved method for identifying pylon types based on similarity measurement and a linearly transformed dataset. It begins by simplifying the identification of point clouds using the pylon shape curve. Subsequently, the resemblance between the curve and those curves within the dataset is evaluated using a similarity measurement to determine the pylon type. A novel method is proposed for calculating the characteristic parameters of the pylon point clouds. The horizontal and vertical distribution characteristics of the pylon point clouds are analyzed to identify key segmentation positions based on their types. Feature points are derived from key segmentation positions to calculate the characteristic parameters. Finally, the pylon 3D models are reconstructed on the basis of the calculated values. The experimental results showed that, compared with other similarity measurements, the Hausdorff distance had the best effect as a similarity measurement using the linearly transformed dataset, with an overall evaluation F-score of 86.4%. The maximum relative error of the calculated pylon parameters did not exceed 5%, affirming the feasibility of the algorithm. [ABSTRACT FROM AUTHOR]

Gaps are openings within tropical forest canopies created by natural or anthropogenic disturbances. Important aspects of gap dynamics that are not well understood include how gaps close over time and their potential for contagiousness, indicating whether the presence of gaps may or may not induce the creation of new gaps. This is especially important when we consider disturbances from selective logging activities in rainforests, which take away large trees of high commercial value and leave behind a forest full of gaps. The goal of this study was to quantify and understand how gaps open and close over time within tropical rainforests using a time series of airborne LiDAR data, attributing observed processes to gap types and origins. For this purpose, the Jamari National Forest located in the Brazilian Amazon was chosen as the study area because of the unique availability of multi-temporal small-footprint airborne LiDAR data covering the time period of 2011–2017 with five data acquisitions, alongside the geolocation of trees that were felled by selective logging activities. We found an increased likelihood of natural new gaps opening closer to pre-existing gaps associated with felled tree locations (<20 m distance) rather than farther away from them, suggesting that small-scale disturbances caused by logging, even at a low intensity, may cause a legacy effect of increased mortality over six years after logging due to gap contagiousness. Moreover, gaps were closed at similar annual rates by vertical and lateral ingrowth (16.7% yr−1) and about 90% of the original gap area was closed at six years post-disturbance. Therefore, the relative contribution of lateral and vertical growth for gap closure was similar when consolidated over time. We highlight that aboveground biomass or carbon density of logged forests can be overestimated if considering only top of the canopy height metrics due to fast lateral ingrowth of neighboring trees, especially in the first two years of regeneration where 26% of gaps were closed solely by lateral ingrowth, which would not translate to 26% of regeneration of forest biomass. Trees inside gaps grew 2.2 times faster (1.5 m yr−1) than trees at the surrounding non-gap canopy (0.7 m yr−1). Our study brings new insights into the processes of both the opening and closure of forest gaps within tropical forests and the importance of considering gap types and origins in this analysis. Moreover, it demonstrates the capability of airborne LiDAR multi-temporal data in effectively characterizing the impacts of forest degradation and subsequent recovery. [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]

Increasing human activities have caused serious disturbance to global forest resources, so how to accurately identify individual trees has become an important task of forest resources investigation. In order to get the accurate number of individual trees, this paper took coniferous forest and mixed coniferous and broad-leaved forest as experimental samples, as well as Digital Orthophoto Map and airborne LiDAR Point Cloud as research data. We propose a deep Learning individual tree segmentation method based on RetinaNet model and PCS algorithm by doing comparative analysis (classical Watershed Algorithm and Layer Stacking Algorithm) at the plots (with high, medium, and low densities). The experimental results show that the method proposed in this paper can solve the problem of individual tree segmentation in high density forest and improve its degree of automation. Compared with Watershed algorithm and Layer stacking algorithm, F-Measure is improved by 6%-29% and 7%-20%, respectively. In other words, the results of individual tree segmentation presented in this paper can not only improve the precision of individual tree segmentation, but also maintain a high detection rate, which can meet the accuracy and high efficiency of individual tree extraction, so as to meet the needs for modern forestry investigation.

In this paper, a knowledge-based algorithm is proposed for automatically generating three-dimensional (3D) building models in dense urban areas by using airborne light detection and ranging (LiDAR) data and aerial images. Automatic 3D building modeling using LiDAR is challenging in dense urban areas, in which houses are typically located close to each other and their heights are similar. This makes it difficult to separate point clouds into individual buildings. A combination of airborne LiDAR and aerial images can be an effective approach to resolve this issue. Information about individual building boundaries, derived by segmentation of images, can be utilized for modeling. However, shadows cast by adjacent buildings cause segmentation errors. The algorithm proposed in this paper uses an improved segmentation algorithm (Susaki, J. 2012.) that functions even for shadowed buildings. In addition, the proposed algorithm uses assumptions about the geometry of building arrangement to calculate normal vectors to candidate roof segments. By considering the segmented regions and the normals, models of four common roof types-gable-roof, hip-roof, flat-roof, and slant-roof buildings-are generated. The proposed algorithm was applied to two areas of Higashiyama ward, Kyoto, Japan, and the modeling was successful even in dense urban areas. [ABSTRACT FROM AUTHOR]

Since the shorelines are important geographical boundaries, monitoring shoreline change plays an important role in integrated coastal management. With the evolution of remote sensing technology, many studies have used optical images to measure and to extract shoreline. However, some factors limit the use of optical imaging on shoreline mapping. Considering that the airborne LiDAR data can provide more accurate topographical information, there are some studies that have been investigated using airborne LiDAR to map shorelines. However, a literature review that combines airborne LiDAR with shoreline measurement and extracting methods has not yet been conducted. The motivation of this paper is to present a narrative review of shoreline mapping by using airborne LiDAR, including a laser scanning system, data availability, and current extraction techniques over the past two decades. Therefore, we conducted a broad search and finally summarized more than 130 articles on airborne LiDAR technology for shoreline measurement and shoreline extraction. We find that shoreline mapping by using airborne LiDAR still meets the challenge, such as objective condition limitations, data availability limitations, and self-characteristic limitations. The current method of shoreline extraction has a great potential to be improved; particularly when combined with the emerging current state-of-the-art LiDAR point cloud processing techniques (e.g., deep-learning algorithms), they will have a brighter future. This review paper provides an overview and the current trend of shoreline mapping using airborne LiDAR, and points out the limitations, challenges, and future opportunities. Moreover, it also can serve as a starting point for novices and experts to study the shoreline mapping by using airborne LiDAR, which provides a scientific support for studying shoreline changes.