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Kinematic laser scanning is a widely-used surveying technique based on light detection and ranging (LiDAR) that enables efficient data acquisition by mounting the laser scanner on a moving platform. In order to obtain a georeferenced point cloud, the trajectory of the moving platform must be accurately known. To this end, most commercial laser scanning systems comprise an inertial measurement unit (IMU) and a global navigation satellite system (GNSS) receiver and antenna. Trajectory estimation is then the task of determining the platform's position and orientation by integrating measurements from the IMU, GNSS, and possibly the laser scanner itself. Here, we present a comprehensive approach to trajectory estimation for kinematic laser scanning, based on batch least-squares adjustment incorporating pre-processed GNSS positions, raw IMU data and plane-based LiDAR correspondences in a single estimation procedure. In comparison to the classic workflow of Kalman filtering followed by strip adjustment, this is a holistic approach with tight coupling of IMU and LiDAR. For the latter, we extend the data-derived stochastic model for the LiDAR plane observations with prior knowledge of the LiDAR measurement process. The proposed trajectory estimation approach is flexible and allows different system configurations as well as joint registration of multiple independent kinematic datasets. This is demonstrated using as a practical example a combined dataset consisting of two independent data acquisitions from crewed aircraft and uncrewed aerial vehicle. All measurements from both datasets are jointly adjusted in order to obtain a single high-quality point cloud, without the need for ground control. The performance of this approach is evaluated in terms of point cloud consistency, precision, and accuracy. The latter is done by comparison to terrestrially surveyed reference data on the ground. The results show improved consistency, accuracy, and precision compared to a standard workflow, with the RMSE reduced from 7.43 cm to 3.85 cm w.r.t. the reference data surfaces, and the point-to-plane standard deviation on the surfaces reduced from 3.01 cm to 2.44 cm. Although a direct comparison to the state-of-the-art can only be made with caution, we can state that the suggested method performs better in terms of point cloud consistency and precision, while at the same time achieving better absolute accuracy. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) mapping is important to achieve early warning for construction safety and support the long-term safety maintenance of tunnels. However, generating 3D point cloud maps of excavation tunnels that tend to be deficient in features, have rough lining structures, and suffer from dynamic construction interference, can be a challenging task. In this paper, we propose a novel legged robot-aided 3D tunnel mapping method to address the influence of point clouds in the mapping phase. First, a method of kinematic model construction that integrates information from both the robot's motors and the inertial measurement unit (IMU) is proposed to correct the motion distortion of point clouds. Then, a residual compensation model for unreliable regions (abbreviated as the URC model) is proposed to eliminate the inherent alignment errors in the 3D structures. The structural regions of a tunnel are divided into different reliabilities using the K-means method, and an inherent alignment metric is compensated based on region residual estimation. The compensated alignment metric is then incorporated into a rotation-guided anomaly consistency detection (RAD) model. An isolation forest-based anomaly consistency indicator is designed to remove anomalous light detection and ranging (LiDAR) points and reduce sensor noise caused by ultralong distances. To verify the proposed method, we conduct numerous experiments in three tunnels, namely, a drilling and blasting tunnel, a TBM tunnel, and an underground pedestrian tunnel. According to the experimental results, the proposed method achieves 0.84 ‰, 0.40 ‰, and 0.31 ‰ closure errors (CEs) for the three tunnels, respectively, and the absolute map error (AME) and relative map error (RME) are approximately 1.45 cm and 0.57 %, respectively. The trajectory estimation and mapping errors of our method are smaller than those of existing methods, such as FAST-LIO2, Faster-LIO and LiLi-OM. In addition, ablation tests are conducted to further reveal the roles of the different models used in our method for legged robot-aided 3D mapping in tunnels. [ABSTRACT FROM AUTHOR]

Accurate and robust simultaneous localization and mapping (SLAM) is essential for autonomous exploration, unmanned transportation, and emergency rescue operations in complex underground spaces. However, the demanding conditions of underground spaces, characterized by poor lighting, weak textures, and high dust levels, pose substantial challenges to SLAM. To address this issue, we propose a graph-based adaptive weighted fusion SLAM (AWF-SLAM) for autonomous robots to achieve accurate and robust SLAM in complex underground spaces. First, a contrast limited adaptive histogram equalization (CLAHE) that combined adaptive gamma correction with weighting distribution (AGCWD) in hue, saturation, and value (HSV) space is proposed to enhance the brightness and contrast of visual images in underground spaces. Then, the performance of each sensor is evaluated using a consistency check based on the Mahalanobis distance to select the optimal configuration for specific conditions. Subsequently, we elaborate an adaptive weighting function model, which leverages the residuals from point cloud matching and the inner point rate of image matching. This model fuses data from light detection and ranging (LiDAR), inertial measurement unit (IMU), and cameras dynamically, enhancing the flexibility of the fusion process. Finally, multiple primitive features are adaptively fused within the factor graph optimization, utilizing a sliding window approach. Extensive experiments were conducted to check the performance of AWF-SLAM using a self-designed mobile robot in underground parking lots, excavated subway tunnels, and complex underground coal mine spaces based on reference trajectories and reconstructions provided by state-of-the-art methods. Satisfactorily, the root mean square error (RMSE) of trajectory translation is only 0.17 m, and the mean relative robustness distance between the point cloud maps reconstructed by AWF-SLAM and the reference point cloud map is lower than 0.09 m. These results indicate a substantial improvement in the accuracy and robustness of SLAM in complex underground spaces. [ABSTRACT FROM AUTHOR]

[Display omitted] Forest disturbances such as wildfires can dramatically alter forest structure and composition, increasing the likelihood of ecosystem changes. Up-to-date and accurate measures of post-disturbance forest recovery in managed forests are critical, particularly for silvicultural planning. Measuring the live and dead vegetation post-fire is challenging because areas impacted by wildfire may be remote, difficult to access, and/or dangerous to survey. The difficulties of post-fire monitoring are compounded by the global increase in the frequency and severity of disturbances, as expansion of disturbed areas also increases the number and size of areas requiring post-disturbance monitoring. Methods that safely, efficiently, and extensively differentiate silviculturally beneficial coniferous growth from barren ground or deciduous shrubs are necessary to inform post-fire forest management. Satellite imagery can detect burn patterns, but monitoring changes in forest structure post fire is challenging due to complex vegetation responses. To overcome this challenge, this study combines post-disturbance spectral trajectory measures from a time series of historical Landsat imagery with field and remotely piloted aircraft (RPA) lidar (light detection and ranging) data to examine vegetation recovery of lodgepole pine (Pinus contorta) dominated sub-boreal forests after high-severity fires in 2006 in central British Columbia, Canada. Distinct spectral recovery trajectories were identified using data-clustering from a combination of seven Landsat spectral indices, with trajectories varying by recovery magnitude and rate. The forest structure associated with each distinct trajectory of spectral recovery was analyzed using 430 ha of spatially explicit forest structure measures (e.g., basal area, stem counts) and composition (e.g., percent coniferous) derived from 26 coincident field plots and high density RPA lidar (>200 points/m2) data. By comparing spectral trajectories to forest structure measures, we found the most spatially abundant cluster of spectral recovery coincided with a basal area of 0.62 m2/ha, high stem densities (>5000 stems/ha) and a high abundance of coniferous trees (>95 % coniferous). Around 10 % of the landscape was associated with relatively high abundance of deciduous vegetation (>20 %) in addition to very high conifer stem densities (>8000 stems/ha). By identifying the structural characteristics associated with unique Landsat spectral trajectories, we highlight the combined value of RPA lidar data and satellite image time series in providing a detailed and spatially explicit characterization of post-fire recovery relevant to managed forests. [ABSTRACT FROM AUTHOR]

Accurate and timely information on winter wheat spatial distribution is essential for food security and environmental sustainability. However, high-quality nation-wide winter wheat products at high resolutions are still scarce around the world, and the approaches for winter wheat mapping are generally constrained by the lack of sufficient and representative training data. In this study, a knowledge-based approach based on spectral and polarization information from critical stages of winter wheat, was proposed to extract high-quality training data of winter wheat, thereby supporting winter wheat mapping with machine learning classifiers. Additionally, classification model trained by the generated training data was transferred across years to achieve the in-season mapping of winter wheat. Two-year classification scenarios based on the automated training data generation (ATDG) or model transfer (MT) were designed to evaluate the quality of automatically generated training data, the performance of model transfer, the contribution of optical and radar data, and the earliest timing for winter wheat mapping over China. With the ATDG and MT, the first 10-m resolution maps of winter wheat over China (ChinaWheat10) were produced for three consecutive years (2020 & 2021 by ATDG; 2021 & 2022 by MT). For ATDG and MT, the combined features of Sentinel-1 and Sentinel-2 yielded the highest overall accuracies with the random forest classifier. Specifically, winter wheat mapping with the ATDG achieved the highest F1-score of 0.94 for both 2020 and 2021. The MT reached a comparable F1-score of 0.94 and 0.93 for 2021 and 2022, and winter wheat maps with the F1-score of 0.93 and 0.92 could be produced as early as April (two months ahead of harvesting). Besides well-delineated winter wheat parcels, the estimated areas of ChinaWheat10 aligned well with the agricultural census data at the provincial (R2 ≥ 0.95) and municipal (R2 ≥ 0.91) levels. These findings suggest the proposed approaches have a great potential for accurate, cost-effective and high-resolution in-season mapping of winter wheat over large regions. [ABSTRACT FROM AUTHOR]

Accurately orientated and matched images and light detection and ranging (LiDAR) point clouds are becoming the new standard for many applications in geomatics. Large data volumes and different observation types make scalability and efficient optimization challenging, particularly for matching based on variants of the iterative closest point algorithm. Here, we present a method to address this by temporally segmenting the pre-processed global navigation satellite system/inertial navigation system solution and representing LiDAR data as voxels with metadata. The efficiency, accuracy, and scalability of different voxel sizes have been assessed in this study. Hybrid adjustment of image tie-points and voxels from LiDAR data optimized the trajectory and sensor corrections for both LiDAR scanners and cameras in the presented experiment. A little over one billion LiDAR points and ∼ 3000 images were matched in < 1. 5 h and showed high accuracy in the range of a few millimeters. This accuracy is comparable to the existing methods for hybrid image and LiDAR adjustment. [ABSTRACT FROM AUTHOR]

Light detection and ranging (LiDAR)-derived point cloud has become the standard spatial data for digital terrain model (DTM) construction; however, it suffers from huge data with much redundant information due to the oversampling. This often causes considerable inconvenience in the downstream data processing. To this end, an adaptive coarse-to-fine clustering and terrain feature-aware-based method is proposed to reduce data points in the context of terrain modeling in this paper. Firstly, a coarse-to-fine clustering method with the consideration of terrain complexity is developed to adaptively cluster LiDAR terrain points. Then, according to the geometric properties of terrain breaklines, a terrain feature-aware multi-strategy method is presented to pick representative points in the clusters. Finally, important boundary points including inflection point on the boundary curve and critical point on terrain features are further selected. The proposed method is compared with seven state-of-the-art point cloud simplification methods under six data reduction ratios on six plots with different terrain characteristics. Results indicate that the proposed method obtains a good balance between terrain-feature preservation and uniform distribution of data points. Compared to the state-of-the-art methods, the proposed method reduces the average root mean square errors (absolute errors) of the DTMs by 12.2%-51.7% (7.69%-83.8%) on the six plots. Moreover, the proposed method obtains the mean terrain slope and terrain roughness more reasonably approximate to the references. In short, the newly developed method can be considered as an alternative tool to select representative points from the huge remote-sensing-derived point cloud in the context of DTM production. [ABSTRACT FROM AUTHOR]

As an effective complement to common laser scanning systems, the portable laser scanning system can acquire point clouds flexibly and quickly. Calibration between Light detection and ranging (LiDAR) sensors and inertial measurement units (IMU) is the prerequisite for laser scanning systems to obtain high-quality point clouds. Related methods have been proposed in the last two decades, where the global navigation satellite system (GNSS) or high-precision calibration fields are commonly used. However, the extrinsic self-calibration of LiDAR-IMU is challenging, due to the large distortion in single-frame point cloud caused by rapid motion and the position errors of IMU integration which drift quickly. At the same time, the highly dynamic motion patterns of portable devices and the changes in the scanned scene structure are not well considered in existing LiDAR odometry methods. To take better advantage of the characteristics of non-repetitive scanning LiDAR sensor, this paper proposes AFLI-Calib, which utilizes adaptive frame length LiDAR odometry to perform the extrinsic self-calibration of LiDAR-IMU. Unlike LiDAR odometry methods with a fixed frame length, the LiDAR frame length is dynamically adjusted according to the motion state of sensors and the matching stability of scenes. The single-frame point cloud is registered to the map through a linear-based continuous-time model, eliminating the motion distortion correction in advance. For further optimization of trajectory and extrinsic parameters, IMU raw measurements and LiDAR observations are involved in the multi-constraint optimization, through tightly-coupled IMU pre-integration constraints, LiDAR point-to-plane constraints, and prior constraints. The method is fully validated using self-collected calibration data of indoor and outdoor scenes and different motion modes. Experiments show that on the test data, the translation parameter accuracy of the method is 0.041 m, which is 56.3% higher than the state-of-the-art method. The standard deviation is significantly reduced, with translation deviation (0.017 m, 0.024 m, 0.022 m) and rotation deviation (0.17°, 0.25°, 0.15°), which verifies the robustness of our method. The average RMSE of distances to the reference point cloud acquired by the terrestrial laser scanning system (TLS) is 0.042 m, showing a high accuracy calibration result. Comparative experiments with the fixed frame length LiDAR odometry method and classical "correction-then-registration" motion distortion model further verify the superiority and effectiveness of the proposed adaptive frame length LiDAR odometry. [ABSTRACT FROM AUTHOR]

Vegetation cover fraction (fCover) and related quantities are basic yet critical vegetation structure variables in various disciplines and applications. Ground- and aerial-based proximal and remote sensing techniques have been widely adapted across multiple spatial extents. However, the definitions of fCover-related nomenclatures have not yet been fully standardized, leading to confusing terms and making comparing historic measures difficult. With the issues potentially arising from an increasing diversity of fCover and related quantities estimation methods and corresponding uncertainties, there is also a growing need to spread knowledge on the current advances, challenges, and perspectives, especially in the context of no such existing review for ground- and aerial- based estimation. This paper provides the current knowledge mainly concerning passive image-based methods and active light detection and ranging (LiDAR) -based methods. We first harmonized the definitions of fCover and its related quantities (e.g., effective canopy cover, crown cover, stratified vegetation cover, and canopy fraction). Secondly, the typical applications of fCover and related quantities over a range of scales, fields, and ecosystems were summarized. Thirdly yet importantly, we offered a comprehensive review of traditional non-imaging methods, image-based methods (e.g., segmentation, unmixing, and spectral retrieval), point cloud-based methods (e.g., rasterization), and LiDAR return-based methods (e.g., return number index and return intensity retrieval) across different platforms (i.e., ground, unmanned aerial vehicle (UAV) and airplane). Our investigation of fCover and related quantities estimation touches upon various vegetation ecosystems, including agriculture cropland, grassland, wetland, and forest. Finally, the current challenges and future directions were discussed, such as image signal processing under complex heterogeneous surfaces and stratified cover and non-photosynthesis cover retrieval. We, therefore, expect that this review may offer an insight into fCover and related quantities estimation and serve as a reference for remote sensing scientists, agronomists, silviculturists, and ecologists. [ABSTRACT FROM AUTHOR]

Terrestrial hyperspectral light detection and ranging (LiDAR) (HSL), as one novel LiDAR system integrated with more wavelengths, has supplied enhanced possibilities in many fields by delivering the geographic coordinate and the hyperspectral backscattered intensity simultaneously. However, like the traditional single-wavelength LiDAR, it is faced with the distance effect, which restricts the accurate acquisition of the hyperspectral intensities and the quantitative retrieval of characteristics of the targets. The previously reported distance effect correction models mainly concentrated on the distance effect correction for the single-wavelength LiDAR system, and some normalization methods with the standard reflectance panel were also provided to calibrate the data measured by multispectral LiDAR and HSL, producing the reflectance-related value. Nevertheless, it is valuable to systematically explore the distance effect of HSL over long distances and search for the corresponding correction methods. Along these lines, in this study, the homogeneous targets and standard reflectance panels with different reflectance values were employed to participate in the distance experiment to systematically explore and analyze the distance effect phenomenon on terrestrial HSL data. A novel practical piecewise fitting model was proposed to output realistic correction results and it was also compared with the classic inverse distance square law and polynomial fitting model. To further demonstrate the feasibility and good capability, the models were employed in the intensity correction for the various vegetation leaf species. A simple 3-D scene experiment was then carried out to demonstrate the intensity variation before and after the implementation of the distance effect correction. From the acquired result, it suggests that (1) the distance effect on terrestrial HSL data is not related to the wavelength and the scanned target. All the wavelengths share a common distance effect; (2) the concept of the 'distance effect function' could represent well the distance effect for the terrestrial HSL; (3) as far as the correction ability is concerned, the proposed piecewise fitting model achieved better performance than the others because it exhibited the smallest standard deviation (σ), coefficient of variation (CV), and the ratio of the coefficient of variation (ε) for the correction results of the homogeneous targets, and the leaf experiments confirmed it again; and (4) the further 3-D experiment result exhibited the intensity variation. It proves that the distance effect does exist in the 3-D scanning results and the correction is necessary. Besides, for small-scanned scenes, the incidence angle effect dominates and largely affects the intensity data instead of the distance effect. This study enriches the study of the distance effect mechanism on the terrestrial HSL data, which facilitates the development of research on the radiative effect correction of the HSL. At the same time, the proposed model can be adopted for the analysis and correction of the distance effect on terrestrial HSL data in actual scanning tasks. [ABSTRACT FROM AUTHOR]

LiDAR (short for "Light Detection And Ranging" or "Laser Imaging, Detection, And Ranging") technology can be used to provide detailed three-dimensional elevation maps of urban and rural landscapes. The geographically granular and open-source nature of this data lends itself to an array of societal, organisational and business applications where geo-demographic type data is utilised. However, the complexity involved in processing this multi-dimensional data in raw form has thus far restricted its practical adoption. This paper proposes a series of convenient task-agnostic tile elevation embeddings to address this challenge, using recent advances from unsupervised Deep Learning. We test the potential of our embeddings by predicting seven English indices of deprivation (2019) for small geographies in the Greater London area. These indices cover a range of socio-economic outcomes and serve as a proxy for a wide variety of potential downstream tasks to which the embeddings can be applied. We consider the suitability of this data not just on its own but also as an auxiliary source of data in combination with demographic features, thus providing a realistic use case for the embeddings. Having trialled various model/embedding configurations, we find that our best performing embeddings lead to Root-Mean-Squared-Error (RMSE) improvements of up to 21% over using standard demographic features alone. We also demonstrate how our embedding pipeline, using Deep Learning combined with K-means clustering, produces coherent tile segments which allow the latent embedding features to be interpreted. [ABSTRACT FROM AUTHOR]

• PASTIS-R first open-access multimodal dataset with panoptic annotations. • Thorough evaluation of different fusion schemes on three crop mapping tasks. • Multimodality brings consistent improvement in performance and cloud robustness. Optical and radar satellite time series are synergetic: optical images contain rich spectral information, while C-band radar captures useful geometrical information and is immune to cloud cover. Motivated by the recent success of temporal attention-based methods across multiple crop mapping tasks, we propose to investigate how these models can be adapted to operate on several modalities. We implement and evaluate multiple fusion schemes, including a novel approach and simple adjustments to the training procedure, significantly improving performance and efficiency with little added complexity. We show that most fusion schemes have advantages and drawbacks, making them relevant for specific settings. We then evaluate the benefit of multimodality across several tasks: parcel classification, pixel-based segmentation, and panoptic parcel segmentation. We show that by leveraging both optical and radar time series, multimodal temporal attention-based models can outmatch single-modality models in terms of performance and resilience to cloud cover. To conduct these experiments, we augment the PASTIS dataset (Garnot and Landrieu, 2021a) with spatially aligned radar image time series. The resulting dataset, PASTIS-R, constitutes the first large-scale, multimodal, and open-access satellite time series dataset with semantic and instance annotations. (Dataset available at: https://zenodo.org/record/5735646) [ABSTRACT FROM AUTHOR]

Land cover is an integral component for characterizing anthropogenic activity and promoting sustainable land use. Mapping distribution and coverage of land cover at broad spatiotemporal scales largely relies on classification of remotely sensed data. Although recently multi-source data fusion has been playing an increasingly active role in land cover classification, our intensive review of current studies shows that the integration of optical, synthetic aperture radar (SAR) and light detection and ranging (LiDAR) observations has not been thoroughly evaluated. In this research, we bridged this gap by i) summarizing related fusion studies and assessing their reported accuracy improvements, and ii) conducting our own case study where for the first time fusion of optical, radar and waveform LiDAR observations and the associated improvements in classification accuracy are assessed using data collected by spaceborne or appropriately simulated platforms in the LiDAR case. Multitemporal Landsat-5/Thematic Mapper (TM) and Advanced Land Observing Satellite-1/ Phased Array type L-band SAR (ALOS-1/PALSAR) imagery acquired in the Central New York (CNY) region close to the collection of airborne waveform LVIS (Land, Vegetation, and Ice Sensor) data were examined. Classification was conducted using a random forest algorithm and different feature sets in terms of sensor and seasonality as input variables. Results indicate that the combined spectral, scattering and vertical structural information provided the maximum discriminative capability among different land cover types, giving rise to the highest overall accuracy of 83% (2–19% and 9–35% superior to the two-sensor and single-sensor scenarios with overall accuracies of 64–81% and 48–74%, respectively). Greater improvement was achieved when combining multitemporal Landsat images with LVIS-derived canopy height metrics as opposed to PALSAR features, suggesting that LVIS contributed more useful thematic information complementary to spectral data and beneficial to the classification task, especially for vegetation classes. With the Global Ecosystem Dynamics Investigation (GEDI), a recently launched LiDAR instrument of similar properties to the LVIS sensor now operating onboard the International Space Station (ISS), it is our hope that this research will act as a literature summary and offer guidelines for further applications of multi-date and multi-type remotely sensed data fusion for improved land cover classification. [ABSTRACT FROM AUTHOR]

This paper describes a deep-learning-based unsupervised forest height estimation method based on the synergy of the high-resolution L-band repeat-pass Polarimetric Synthetic Aperture Radar Interferometry (PolInSAR) and low-resolution large-footprint full-waveform Light Detection and Ranging (LiDAR) data. Unlike traditional PolInSAR-based methods, the proposed method reformulates the forest height inversion as a pan-sharpening process between the low-resolution LiDAR height and the high-resolution PolSAR and PolInSAR features. A tailored Generative Adversarial Network (GAN) called PolGAN with one generator and dual (coherence and spatial) discriminators is proposed to this end, where a progressive pan-sharpening strategy underpins the generator to overcome the significant difference between spatial resolutions of LiDAR and SAR-related inputs. Forest height estimates with high spatial resolution and vertical accuracy are generated through a continuous generative and adversarial process. UAVSAR PolInSAR and LVIS LiDAR data collected over tropical and boreal forest sites are used for experiments. Ablation study is conducted over the boreal site evidencing the superiority of the progressive generator with dual discriminators employed in PolGAN (RMSE: 1.21 m) in comparison with the standard generator with dual discriminators (RMSE: 2.43 m) and the progressive generator with a single coherence (RMSE: 2.74 m) or spatial discriminator (RMSE: 5.87 m). Besides that, by reducing the dependency on theoretical models and utilizing the shape, texture, and spatial information embedded in the high-spatial-resolution features, the PolGAN method achieves an RMSE of 2.37 m over the tropical forest site, which is much more accurate than the traditional PolInSAR-based Kapok method (RMSE: 8.02 m). [ABSTRACT FROM AUTHOR]

3D mapping is now essential for urban infrastructure resource monitoring, autonomous driving, and the fulfillment of digital earth. LiDAR-based Simultaneous Localization And Mapping (SLAM) technology has been widely studied because of its efficient application in the reconstruction of the 3D environments that benefitted from its sensor characteristic. However, the sparsity of low cost LiDAR (Light Detection And Ranging) sensor provides great challenge for it. Insufficient or poor distribution of the constraints as well as the motion distortion in one scan of point cloud would deteriorate the pose estimation in SLAM. In this paper, a novel 3D mapping method based only on the LiDAR data is proposed to enhance the performance of SLAM-based 3D point clouds map reconstruction. The proposed system consists of two main modules: localization module for rapid ego-motion estimation within the surrounding environment, and mapping module for accurate point clouds fusion. As for the localization, scan-to-map point cloud registration scheme are adopted where structural features are firstly extracted to provide valid correspondences. What's more, an effective method is figured out to balance the cost factors for the optimization function of pose estimation in the process. This further handles the degenerated cases for cloud registration. Focusing on improving the quality of the map, a spline motion model is integrated with non-rigid point cloud fusion to facilitate the continuous and nonrepetitive mapping of the environment. Extensive experiments are carried out for the verification of our system including large-scale outdoor and long-corridor scenes. According to the experimental results, our system has achieved 0.78‰closure error and 27.5 % improvement in the APE (Absolute Pose Errors), outperforming the state-of-the-art LiDAR-based SLAM (e.g. LeGO-LOAM) frameworks in our test sites. To specifically verify the performance of 3D mapping, the precision of 3D points is quantified through comparing with the high-precision point cloud maps collected by MLS (Mobile Laser Scanning) and TLS (Terrestrial Laser Scanning). The RMS of point-to-plane distances have improved 20 % based on the fitted Weibull distribution. In addition, some ablation tests are conducted to reveal the efficacy of different components in our system. [ABSTRACT FROM AUTHOR]

Airborne laser bathymetry (ALB) is employed to measure shallow depth water by using a high sampling rate and point density. Thus, the problems of using traditional detection methods in inaccessible areas can be avoided. This study focuses on practical solutions for receiving echo signals, identifying target echoes, and integrating land and underwater terrain point cloud data in coastal environments. Optimization of the system design and its data processing scheme is undertaken to improve the performance of the receiving system based on a single-band ALB system developed by the authors at Shenzhen University. A flight experiment over eastern Hainan Island was conducted, during which the effectiveness of the proposed strategy was verified. Finally, the technical characteristics of the self-developed system are summarized to provide a reliable reference source for the subsequent industrialization and production of related marine light detection and ranging (LiDAR) laser systems. [ABSTRACT FROM AUTHOR]

The complementary fusion of light detection and ranging (LiDAR) data and image data is a promising but challenging task for generating high-precision and high-density point clouds. This study proposes an innovative LiDAR-guided stereo matching approach called LiDAR-guided stereo matching (LGSM), which considers the spatial consistency represented by continuous disparity or depth changes in the homogeneous region of an image. The LGSM first detects the homogeneous pixels of each LiDAR projection point based on their color or intensity similarity. Next, we propose a riverbed enhancement function to optimize the cost volume of the LiDAR projection points and their homogeneous pixels to improve the matching robustness. Our formulation expands the constraint scopes of sparse LiDAR projection points with the guidance of image information to optimize the cost volume of pixels as much as possible. We applied LGSM to semi-global matching and AD-Census on both simulated and real datasets. When the percentage of LiDAR points in the simulated datasets was 0.16%, the matching accuracy of our method achieved a subpixel level, while that of the original stereo matching algorithm was 3.4 pixels. The experimental results show that LGSM is suitable for indoor, street, aerial, and satellite image datasets and provides good transferability across semi-global matching and AD-Census. Furthermore, the qualitative and quantitative evaluations demonstrate that LGSM is superior to two state-of-the-art optimizing cost volume methods, especially in reducing mismatches in difficult matching areas and refining the boundaries of objects. [ABSTRACT FROM AUTHOR]

Place recognition is widely used for global localization technology. However, the existing place recognition solutions are limited by the requirement for the same type of sensors to be used in both the localization process and the mapping process. Therefore, the existing heterogeneous 3D map cannot be used for place recognition directly with the existing methods, leading to underutilization of information. In addition, most of the existing global feature descriptors used in place recognition solutions are still not highly descriptive and perform poorly under changed viewpoint scenes. To resolve these challenges, this paper presents a place recognition solution using virtual light detection and ranging (LiDAR) and polar grid height coding image (PGHCI) descriptors in the existing heterogeneous 3D map. First, virtual LiDAR is proposed to generate a series of virtual scans that are similar to the real scan of the localization sensor from the existing map, overcoming the limitation of the existing place recognition methods. Next, a novel PGHCI descriptor for place recognition is generated, and a method that overcomes the recognition difficulty of changed viewpoints in the same scene is presented. Two weighted distances for similarity estimation are analyzed, and the performance of the PGHCI descriptor with different parameters is evaluated. Finally, the performance of the PGHCI descriptor and the solution proposed in the paper is evaluated on several popular benchmark datasets and our own dataset. Comprehensive experiments demonstrated that the PGHCI descriptor has higher descriptiveness and is robust with respect to the changed viewpoint scene, as shown by the comparison of precision-recall (PR) curves using datasets with multiple scenes. The proposed place recognition solution has 100% success rates in the evaluation exclude under occluded conditions, showing that it is feasible to achieve robust place recognition using a heterogeneous 3D map. [ABSTRACT FROM AUTHOR]

The absence of large archaeological settlements in the Eastern Arabian Peninsula suggests that most of the recent wet periods occurring during climate oscillations in these areas were short-lived. However, a ∼ 2-by-3-km buried rectangular feature was discovered opportunistically in the southeast of the Qatar Peninsula while performing radar subsurface mapping of structural conduits for groundwater characterization. The feature is characterized by its unique contrasting patterns of surface and subsurface roughness as well as its organic-rich mixture of soils, as assessed from high-resolution radar and multispectral observations, respectively, and validated with GPR and EM subsurface probing. The above characteristics could be the only remaining evidence for a large primitive settlement due to the harsh erosive setting. To explore the natural or anthropogenic origin of this anomalous feature, we compare its dimensions, structural setting, roughness, and composition to similar formations in the Qatar Peninsula and other arid karst environments. Findings suggest that the feature is texturally and compositionally unique within the Qatar Peninsula, and is not reproduced in other arid karst terrains nor reproduced by the fracture patterns associated with the Qatar-South Fars Arch. We hypothesize that the feature is a karst depression modified by semi-stationary primitive human settlements ∼ 3640 BP or earlier based on radiocarbon dating of in-situ charcoal samples. Moreover, the organic-rich mixture of soil and the occurrence of several symmetrical features observed inside the perimeter suggest that landscaping, farming and irrigation activities could have occurred within the site, further suggesting that long-lasting wet conditions—with sufficient fresh water resources or reliance on karst aquifers—were able to sustain a relatively large primitive settlement not previously witnessed in the region's archaeological record. Future improvements in the resolution and coverage of orbital L-band radar observations, as used above, can provide new insights into understanding prehistoric human occupation under fluctuating climatic conditions and groundwater dynamics in these hyper-arid regions. [ABSTRACT FROM AUTHOR]

The method presented herein performs the tasks of identification, modeling, and registration refinement of poles using Light Detection And Ranging (LiDAR) point clouds. This method starts with a preliminary segmentation followed by eliminating outliers by integrating Fourier series and robust estimators. Poles' cross sectional shapes are then identified by a customized template matching. Afterwards, points belonging to poles are extracted and the as-built model and registration-refined version of poles are generated. The algorithm was tested on five datasets including point clouds of three industrial sites, an urban environment, and a pole-like monument with very different volumes, resolutions, and configurations. The developed method works robustly despite the challenges that are often present in LiDAR data comprising outliers, non-uniform point sampling, gaps, and registration error. The obtained results indicate that 116 out of 117 poles in all five datasets were identified with no false positives at the object level. At point level, it reached greater than 98% average precision and 98% average accuracy in all datasets. Poles were modeled with a sub-millimeter precision in all datasets except for the third dataset in which poles were modeled with millimeter precision. [ABSTRACT FROM AUTHOR]

Co-registration of aerial imagery and Light Detection and Ranging (LiDAR) data is quite challenging because the different imaging mechanisms produce significant geometric and radiometric distortions between the two multimodal data sources. To address this problem, we propose a robust and effective coarse-to-fine registration method that is conducted in two stages utilizing spatial constraints and Gabor structural features. In the first stage, the LiDAR point cloud data is transformed into an intensity map that is used as the reference image. Then, coarse registration is completed by designing a partition-based Features from Accelerated Segment Test (FAST) operator to extract the uniformly distributed interest points in the aerial images and thereafter performing a local geometric correction based on the collinearity equations using the exterior orientation parameters (EoPs). The coarse registration aims to provide a reliable spatial geometry relationship for the subsequent fine registration and is designed to eliminate rotation and scale changes, as well as making only a few translation differences exist between the images. In the second stage, a novel feature descriptor called multi-Scale and multi-Directional Features of odd Gabor (SDFG) is first built to capture the multi-scale and multi-directional structural properties of the images. Then, the three-dimensional (3D) phase correlation (PC) of the SDFG descriptor is established to detect the control points (CPs) between the aerial and LiDAR intensity image in the frequency domain, where the image matching is accelerated by the 3D Fast Fourier Transform (FFT) technique. Finally, the obtained CPs not only are employed to refine the EoPs, but also are used to achieve the fine registration of the aerial images and LiDAR data. We conduct experiments to verify the robustness of the proposed registration method using three sets of aerial images and LiDAR data with different scene coverage. Experimental results show that the proposed method is robust to geometric distortions and radiometric changes. Moreover, it achieves the registration accuracy of less than 2 pixels for all cases, which outperforms the current four state-of-the-art methods, demonstrating its superior registration performance. [ABSTRACT FROM AUTHOR]

Advances in fire behaviour modelling provide a catalyst for the development of next generation fuel inputs. Fire simulations underpin risk and consequence mapping and inform decisions regarding ecological and social impacts of different fire regimes. Unoccupied Aerial Systems (UAS) carrying Light Detection and Ranging (LiDAR) sensors have been proposed as a source of structural information with potential for describing fine fuel properties. Whilst these systems have been shown to be capable of describing general vegetation distribution, the ability to distinguish between vegetation elements that contribute to fire spread and those that do not (such as large woody elements) is yet to be explored. This study evaluates the ability of UAS LiDAR point clouds to provide a description of crown fuel elements in eucalypt trees. This is achieved through comparison with dense Terrestrial Laser Scanning (TLS) that were manually attributed with a fuel description. Using the TLSeparation package TLS and UAS LiDAR point clouds achieved 84.6% and 81.1% overall accuracy respectively in the separation of crown fuel and wood in nine reference trees. When applying the same separation process across a 30 by 50 m plot consisting of approximately 75 trees, total canopy fuel volume was found to be strongly correlated between the TLS and UAS LiDAR point clouds (r: 0.96, RMSE: 1.53 m3). A lower canopy base height and greater distance between crown fuel regions within each crown supported visual inspection of the point clouds that TLS point clouds were able to represent the crown to a greater extent than UAS LiDAR point clouds. Despite these differences it is likely that a less complete representation of canopy fuel such as that generated from UAS LiDAR point clouds will suitably represent the crown and canopy fuel objects effectively for fire behaviour modelling purposes. The research presented in this manuscript highlights the potential of TLS and UAS LiDAR point clouds to provide repeatable, accurate 3D characterisation of canopy fuel properties. [ABSTRACT FROM AUTHOR]

Cotton is an important cash crop in the world, as the main source of natural and renewable fiber for textiles. Accurate and timely monitoring of the cotton distribution is crucial for cotton cultivation management and international trade. However, most of the previous researches on cotton identification using remotely sensed images are highly dependent on training samples, and the collection of samples is time-consuming and expensive. To overcome this limitation, a new index, termed as Cotton Mapping Index (CMI), was developed in this study for automatic cotton mapping using time series of Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 Multispectral Instrument (MSI) satellite data. Four sites in the United States (U.S.) and four sites in China were selected to develop and assess the performance of the CMI. The spectral characteristics derived from Sentinel-2 and backscattering coefficients derived from Sentinel-1 for cotton and non-cotton crops during the cotton growth period were analyzed. Considering the phenology differences of crops in different regions, the features at an adaptive window were adopted to construct the CMI. The results showed that at the peak greenness period, the multiplication of red-edge 1 and red-edge 2 band for cotton samples were much larger than those for non-cotton samples, whereas the spectral angle at the red band as well as the absolute values of backscattering coefficients in vertical transmit and vertical receive (VV) polarization for cotton samples were much smaller than those for non-cotton samples. Based on these findings, the CMI was developed to identify cotton cultivated area within the cropland area. The overall accuracy of classification results for the sites in the U.S. was higher than 81.20%, and the mean relative error for the sites in Xinjiang of China was 26.69%. The CMI, which incorporated optical and radar features, had a better performance than the indices using optical features solely. The advantage of the CMI over supervised classifiers (i.e., k-nearest neighbors, support vector machine and random forest) is that no training samples are required. Moreover, the cotton distribution map can be obtained before the harvest using the CMI. These results indicated the potential of the CMI for cotton mapping. The applicability of CMI in other regions with different cropping systems and crop types needs to be further assessed in the future study. [ABSTRACT FROM AUTHOR]

To derive shallow water bathymetry for coastal areas, a common approach is to deploy a scanning airborne bathymetric light detection and ranging (LiDAR) system or a shipborne echosounder for ground surveys. However, recent advancements in satellite remote sensing, including the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) offer new tools for generating satellite derived bathymetry (SDB). The key payload onboard ICESat-2 is the Advanced Topographic Laser Altimeter System (ATLAS), a micro-pulse, photon-counting LiDAR system, simultaneously emitting six separate 532 nm beams at 10 kHz pulse rate. However, despite its high resolution, the major limitation for bathymetry is that ICESat-2 only provides along-track height profiles, leaving observation gaps between the parallel ground tracks. Merging ICESat-2 observations with optical multispectral imagery, as demonstrated herein, provides an effective solution for deriving a full scene of water depth in light of the spectral attenuation behavior. This study aims to combine ICESat-2 and Sentinel-2 optical data to derive shallow water bathymetry (depth <20 m) at six islands and reefs in the South China Sea. ICESat-2 ATL03 point clouds of georeferenced photons are first filtered to determine the seafloor elevation along the ground track. Results indicate a root-mean-square error (RMSE) of 0.26–0.61 m as compared with independent observations from an airborne LiDAR campaign. Next, three semi-empirical functions, namely the Modified Linear/Polynomial/Exponential Ratio Models with its kernel formed by the log ratio between Sentinel-2′s green and blue bands, are used to fit the spectral data with ICESat-2 height profiles. After water depth mapping using the trained model, independent ICESat-2 point clouds are used to validate the Sentinel-2 derived bathymetry. The RMSE values of the three models using the weighted average of multiple images for these six islands are within 0.50–0.90 m in 0–15 m deep. We also demonstrate that a synthesis of satellite laser altimetry and optical remote sensing can produce SDB results that potentially meet the requirement of category C in Zones of Confidence (ZOC) of the Electronic Navigational Chart (ENC) in 0–8 m deep. It is foreseen that ICESat-2 will be a helpful tool for mapping coastal and shallow waters around the world especially where bathymetric data are unavailable. [ABSTRACT FROM AUTHOR]

Semantic segmentation of 3D Light Detection and Ranging (LiDAR) indoor point clouds using deep learning has been an active topic in recent years. However, most deep neural networks on point clouds conduct multi-level feature fusion via a simple U-shape architecture, which lacks enough capacity on both classification and localization in the segmentation task. In this paper, we propose a Neural Architecture Search (NAS) method to search a Feature Pyramid Network (FPN) module for 3D indoor point cloud semantic segmentation. Specifically, we aim to automatically find an effective feature pyramid architecture as a feature fusion neck in a designed novel pyramidal search space covering all information communication paths for multi-level features. The searched FPN module, named SFPN, contains the most important connections among all the potential paths to fuse representations at different levels. Our proposed SFPN is generic and effective as well as capable to be added to existing segmentation networks to augment the segmentation performance. Extensive experiments on ScanNet and S3DIS show that consistent and remarkable gains of segmentation performance can be achieved by different classical networks combined with SFPN. Specially, PointNet++-SFPN achieves mIoU gains of 7.8% on ScanNet v2 and 4.7% on S3DIS, and PointConv-SFPN achieves 4.5% and 3.7% improvement respectively on the above datasets. [ABSTRACT FROM AUTHOR]

Planning and managing urban forests for livable cities remains a challenge worldwide owing to sparse information on the spatial distribution, structure and composition of urban trees and forests. National and municipal sources of tree inventory remain limited due to a lack of detailed, consistent and frequent inventory assessments. Despite advancements in research on the automation of urban tree mapping using Light Detection and Ranging (LiDAR) or high-resolution satellite imagery, in practice most municipalities still perform labor-intensive field surveys to collect and update tree inventories. We present a robust, affordable and rapid method for creating tree inventories in any urban region where sufficient street-level imagery is readily available. Our approach is novel in that we use a Mask Regional Convolutional Neural Network (Mask R-CNN) to detect and locate separate tree instances from street-level imagery, thereby successfully creating shape masks around unique fuzzy urban objects like trees. The novelty of this method is enhanced by using monocular depth estimation and triangulation to estimate precise tree location, relying only on photographs and images taken from the street. Experiments across four cities show that our method is transferable to different image sources (Google Street View, Mapillary) and urban ecosystems. We successfully detect > 70% of all public and private trees recorded in a ground-truth campaign across Metro Vancouver. The accuracy of geolocation is also promising. We automatically locate public and private trees with a mean error in the absolute position ranging from 4 to 6 m, which is comparable to ground-truth measurements in conventional manual urban tree inventory campaigns. [ABSTRACT FROM AUTHOR]

Airborne light detection and ranging (LiDAR) plays an increasingly significant role in urban planning, topographic mapping, environmental monitoring, power line detection and other fields thanks to its capability to quickly acquire large-scale and high-precision ground information. To achieve point cloud classification, previous studies proposed point cloud deep learning models that can directly process raw point clouds based on PointNet-like architectures. And some recent works proposed graph convolution neural network based on the inherent topology of point clouds. However, the above point cloud deep learning models only pay attention to exploring local geometric structures, yet ignore global contextual relationships among all points. In this paper, we present a global-local graph attention convolution neural network (GACNN) that can be directly applied to the classification of unstructured 3D point clouds obtained by airborne LiDAR. Specifically, we first introduce a graph attention convolution module that incorporates global contextual information and local structural features. The global attention module examines spatial relationships among all points, while the local attention module can dynamically learn convolution weights with regard to the spatial position of the local neighboring points and reweight the convolution weights by inspecting the density of each local region. Based on the proposed graph attention convolution module, we further design an end-to-end encoder-decoder network, named GACNN, to capture multiscale features of the point clouds and therefore enable more accurate airborne point cloud classification. Experiments on the ISPRS 3D labeling dataset show that the proposed model achieves a new state-of-the-art performance in terms of average F1 score (71.5%) and a satisfying overall accuracy (83.2%). Additionally, experiments further conducted on the 2019 Data Fusion Contest Dataset by comparing with other prevalent point cloud deep learning models demonstrate the favorable generalization capability of the proposed model. [ABSTRACT FROM AUTHOR]

Arctic sea ice is constantly moving and covered with low-textured surfaces, making it difficult to generate reliable digital surface models (DSMs) from drone images. The movement of sea ice makes georeferencing of DSMs difficult, and the low-textured surfaces of sea ice cause the uncertainty of image matching. This paper proposes a robust method to generate high-quality DSMs for drifting sea ice. To overcome the challenges, the proposed method introduces four improvements to the object-space-based image-matching pipeline: relative georeferencing to recover the horizontality and scale of sea-ice DSMs using a terrestrial light detection and ranging (LiDAR) dataset, match inspection to verify the matched points using several matching constraints, adaptive search-window adjustment to ensure distinct texture information through simple texture analysis, and robust vertical positioning to reduce the matching uncertainty via matching-indicator modeling. Performance evaluations were conducted with drone and LiDAR datasets obtained from a sea-ice campaign using the Korean Icebreaker Research Vessel (IBRV) Araon in the summer of 2017. The experimental results indicated that the proposed method can achieve significant quality enhancements compared with the existing matching method and that all the considerations contributed significantly to the enhancements. [ABSTRACT FROM AUTHOR]

A big challenge in geodata processing is the seamless and accurate integration of airborne LiDAR (Light Detection And Ranging) and photogrammetric point clouds performed by properly considering their high variations in resolution and precision. In this paper we propose a new approach to co-register airborne point clouds acquired by LiDAR sensors and photogrammetric algorithms, assuming that only dense point clouds from both mapping methods are available, without LiDAR raw data nor flight trajectories. First, semantically segmented point clouds are quality-wise evaluated by assigning sensor-specific quality features to each 3D point. Then, these quality features are aggregated in order to assign a score to each 3D point based on its quality. Finally, using a voxel-based structure, a filtering step is performed to select only the best points used for the registration refinement. We assess the performance of the proposed method on two different case studies to demonstrate its advantages compared to a traditional ICP-based approach. The code of the implemented method is available at https://github.com/3DOM-FBK/HyRe. [ABSTRACT FROM AUTHOR]

Alongside spatial information, an intensity scalar is also measured by LiDAR (Light Detection And Ranging) sensor systems. It corresponds to the amplitude of the backscattered laser beam after reflection on the scanned surface. This information isn't directly usable due to dependencies of geometrical parameters occurring during the scanning process and additional processing modifications. The research community, in a growing trend, invests efforts to convert this signal into a value which could bring qualitative and quantitative new applications. We propose in this paper, a review of the theortical background of LiDAR radiometric calibration, from physical background to state-of-art methodology. A comparison of two approaches to eliminate geometrical (range and incidence angle) and instrumental dependencies of intensity measurement is presented. Their application on several homogeneous areas of a real-world case study shows a significant reduction of intensity variation between surfaces with identical material composition. Finally, a linearization process permits to obtain an equivalent Lambertian reflectance value which, by its disciminant potential, could be specifically suitable for some algorithms like segmentation or registration. [ABSTRACT FROM AUTHOR]

• A multi-level 3D building change detection approach (OGB) for megacities was proposed. • OGB reveals changes in building trajectories (object), morphological parameters (grid), and landscape metrics (block). • A bi-threshold model was developed to capture five building change trajectories. • A set of 3D building landscape metrics were integrated in OGB for block level change monitoring. • OGB was applied to the northern part of Brooklyn, New York, USA. Three-dimensional (3D) building change detection is important for megacities for updating geo-databases, urban sprawl monitoring, disaster assessments and energy budgets. However, few studies examine how to execute the transition of multi-level change monitoring of urban buildings from 2D to 3D. This study presents a new automated Object–Grid–City Block 3D building change detection (OGB) approach that entails the application of multi-temporal aerial Light Detection and Ranging (LiDAR) point clouds. First, building labels at various phases were performed using a graph cuts algorithm to assist with 3D change detection of buildings. Then, we introduced a bi-threshold model to consider and capture trajectories of building change broken down into categories from 1 to 5 and obtain a complete 3D change detection map. In order to reveal the vertical building landscape changes, a set of 3D building landscape metrics was developed for block level change monitoring. Upon examination, the results for the northern part of Brooklyn, New York, USA were confirmed to be robust and refined; the completeness, correctness, and quality values for trajectories 1–5 were 92–95%, 93–97%, 89–95%, respectively. More importantly, the OGB approach can not only effectively monitor intensity, direction (increase or decrease), and spatial pattern changes in 2D and 3D morphological parameters of buildings at the grid level, but can also reflect vertical changes in building structures, and reveal horizontal fragmentations and aggregations of buildings at the block level. [ABSTRACT FROM AUTHOR]

The huge amount of data currently produced by modern Earth Observation (EO) missions has allowed for the design of advanced machine learning techniques able to support complex Land Use/Land Cover (LULC) mapping tasks. The Copernicus programme developed by the European Space Agency provides, with missions such as Sentinel-1 (S1) and Sentinel-2 (S2), radar and optical (multi-spectral) imagery, respectively, at 10 m spatial resolution with revisit time around 5 days. Such high temporal resolution allows to collect Satellite Image Time Series (SITS) that support a plethora of Earth surface monitoring tasks. How to effectively combine the complementary information provided by such sensors remains an open problem in the remote sensing field. In this work, we propose a deep learning architecture to combine information coming from S1 and S2 time series, namely TWINNS (TWIn Neural Networks for Sentinel data), able to discover spatial and temporal dependencies in both types of SITS. The proposed architecture is devised to boost the land cover classification task by leveraging two levels of complementarity, i.e., the interplay between radar and optical SITS as well as the synergy between spatial and temporal dependencies. Experiments carried out on two study sites characterized by different land cover characteristics (i.e., the Koumbia site in Burkina Faso and Reunion Island , a overseas department of France in the Indian Ocean), demonstrate the significance of our proposal. [ABSTRACT FROM AUTHOR]

As the electric companies need to assure the reliability of their services, power line management gains importance during the last years. Many of them rely on LiDAR scanning of their assets to obtain the status of their power line corridors and determine possible risks. In this paper, a novel sevenfold staged pipeline is introduced to classify pylon and wire points and model the conductors. Wire points are subdivided into three categories: shield, common conductor and chain. Pylons of two different types are taken into account: suspension and anchor. For the first case, insulator strains are also identified and separated. Wire points are segmented as individual conductors and a 3D-wise model based on the catenary equation is generated for each conductor using particle swarm optimization. Tests have been conducted on a set with 25 point cloud files to assess the accuracy and correctness of the results given by the proposed pipeline. [ABSTRACT FROM AUTHOR]

Abstract The Bamboo species accounts for almost 1% of the Earth's forested area with an exceptionally fast growth peaking up to 7.5–100 cm per day during the growing period, making it an unique species with respect to measuring and monitoring using conventional forest inventory tools. In addition their widespread coverage and quick growth make them a critical component of the terrestrial carbon cycle and for mitigating the impacts of climate change. In this study, the capability of using airborne Light Detection and Ranging (LiDAR) data for estimating canopy structure and biomass of Moso bamboo (Phyllostachys pubescens) was assessed, which is one of the most valuable and widely distributed bamboo species in the subtropical forests of south China. To do so, we first evaluated the accuracy of using LiDAR data to interpolate the underlying ground terrain under bamboo forests and developed uncertainty surfaces using both LiDAR-derived vegetation and topographic metrics and a Random Forest (RF) classifier. Second, we utilized Principal Component Analysis (PCA) to quantify the variation of the vertical distribution of LiDAR-derived effective Leaf Area Index (LAI) of bamboo stands, and fitted regression models between selected LiDAR metrics and the field-measured attributes such mean height, DBH and biomass components (i.e., culm, branch, foliage and aboveground biomass (AGB)) across a range of management strategies. Once models were developed, the results were spatially extrapolated and compared across the bamboo stands. Results indicated that the LiDAR interpolated DTMs were accurate even under the dense intensively managed bamboo stands (RMSE = 0.117–0.126 m) as well as under secondary stands (RMSE = 0.102 m) with rugged terrain and near-ground dense vegetation. The development of uncertainty maps of terrain was valuable when examining the magnitude and spatial distribution of potential errors in the DTMs. The middle height intervals (i.e., HI4 and HI5) within the bamboo cumulative effective LAI profiles explained more variances by PCA analysis in the bamboo stands. Moso bamboo AGB was well predicted by the LiDAR metrics (R2 = 0.59–0.87, rRMSE = 11.92–21.11%) with percentile heights (h 25 - h 95) and the coefficient of variation of height (h cv) having the highest relative importances for estimating AGB and culm biomass. The h cv explained the most variance in branch and foliage biomass. According to the spatial extrapolation results, areas of relatively low biomass were found on secondary stands (AGB = 49.42 ± 14.16 Mg ha−1), whereas the intensively managed stands (AGB = 173.47 ± 34.16 Mg ha−1) have much higher AGB and biomass components, followed by the extensively managed bamboo stands (AGB = 67.61 ± 13.10 Mg ha−1). This study demonstrated the potential benefits of using airborne LiDAR to accurately derive high resolution DTMs, characterize vertical structure of canopy and estimate the magnitude and distribution of biomass within Moso bamboo forests, providing key data for regional ecological, environmental and global carbon cycle models. [ABSTRACT FROM AUTHOR]

Waveform decomposition is a widely used technique for extracting echoes from full-waveform LiDAR data. Most previous studies recommended the Gaussian decomposition approach, which employs the Gaussian function in laser pulse modeling. As the Gaussian-shape assumption is not always satisfied for real LiDAR waveforms, some other probability distributions (e.g., the lognormal distribution, the generalized normal distribution, and the Burr distribution) have also been introduced by researchers to fit sharply-peaked and/or heavy-tailed pulses. However, these models cannot be universally used, because they are only suitable for processing the LiDAR waveforms in particular shapes. In this paper, we present a new waveform decomposition algorithm based on the B-spline modeling technique. LiDAR waveforms are not assumed to have a priori shapes but rather are modeled by B-splines, and the shape of a received waveform is treated as the mixture of finite transmitted pulses after translation and scaling transformation. The performance of the new model was tested using two full-waveform data sets acquired by a Riegl LMS-Q680i laser scanner and an Optech Aquarius laser bathymeter, comparing with three classical waveform decomposition approaches: the Gaussian, generalized normal, and lognormal distribution-based models. The experimental results show that the B-spline model performed the best in terms of waveform fitting accuracy, while the generalized normal model yielded the worst performance in the two test data sets. Riegl waveforms have nearly Gaussian pulse shapes and were well fitted by the Gaussian mixture model, while the B-spline-based modeling algorithm produced a slightly better result by further reducing 6.4% of fitting residuals, largely benefiting from alleviating the adverse impact of the ringing effect. The pulse shapes of Optech waveforms, on the other hand, are noticeably right-skewed. The Gaussian modeling results deviated significantly from original signals, and the extracted echo parameters were clearly inaccurate and unreliable. The B-spline-based method performed significantly better than the Gaussian and lognormal models by reducing 45.5% and 11.5% of their fitting errors, respectively. Much more precise echo properties can accordingly be retrieved with a high probability. Benefiting from the flexibility of B-splines on fitting arbitrary curves, the new method has the potentiality for accurately modeling various full-waveform LiDAR data, whether they are nearly Gaussian or non-Gaussian in shape. [ABSTRACT FROM AUTHOR]

Filtering of light detection and ranging (LiDAR) data into the ground and non-ground points is a fundamental step in processing raw airborne LiDAR data. This paper proposes an improved progressive triangulated irregular network (TIN) densification (IPTD) filtering algorithm that can cope with a variety of forested landscapes, particularly both topographically and environmentally complex regions. The IPTD filtering algorithm consists of three steps: (1) acquiring potential ground seed points using the morphological method; (2) obtaining accurate ground seed points; and (3) building a TIN-based model and iteratively densifying TIN. The IPTD filtering algorithm was tested in 15 forested sites with various terrains (i.e., elevation and slope) and vegetation conditions (i.e., canopy cover and tree height), and was compared with seven other commonly used filtering algorithms (including morphology-based, slope-based, and interpolation-based filtering algorithms). Results show that the IPTD achieves the highest filtering accuracy for nine of the 15 sites. In general, it outperforms the other filtering algorithms, yielding the lowest average total error of 3.15% and the highest average kappa coefficient of 89.53%. [ABSTRACT FROM AUTHOR]

Terrestrial laser scanning (TLS), widely known as light detection and ranging (LiDAR) technology, is increasingly used to provide highly detailed digital terrain models (DTM) with millimetric precision and accuracy. In order to generate a DTM, TLS data has to be filtered from undesired spurious objects, such as vegetation, artificial structures, etc., Early filtering techniques, successfully applied to airborne laser scanning (ALS), fail when applied to TLS data, as they heavily smooth the terrain surface and do not retain their real morphology. In this article, we present a new methodology for filtering TLS data based on the geometric and radiometric properties of the scanned surfaces. This methodology was built on previous morphological filters that select the minimum point height within a sliding window as the real surface. However, contrary to those methods, which use a fixed window size, the new methodology operates under different spatial scales represented by different window sizes, and can be adapted to different types and sizes of plants. This methodology has been applied to two study areas of differing vegetation type and density. The accuracy of the final DTMs was improved by ∼30% under dense canopy plants and over ∼40% on the open spaces between plants, where other methodologies drastically underestimated the real surface heights. This resulted in more accurate representation of the soil surface and microtopography than up-to-date techniques, eventually having strong implications in hydrological and geomorphological studies. [ABSTRACT FROM AUTHOR]

The rapid development of light detection and ranging (LiDAR) techniques is advancing ecological and forest research. During the last decade, numerous single tree segmentation techniques have been developed using airborne LiDAR data. However, accurate crown segmentation using terrestrial or mobile LiDAR data, which is an essential prerequisite for extracting branch level forest characteristics, is still challenging mainly because of the difficulties posed by tree crown intersection and irregular crown shape. In the current work, we developed a comparative shortest-path algorithm (CSP) for segmenting tree crowns scanned using terrestrial (T)-LiDAR and mobile LiDAR. The algorithm consists of two steps, namely trunk detection and subsequent crown segmentation, with the latter inspired by the well-proved metabolic ecology theory and the ecological fact that vascular plants tend to minimize the transferring distance to the root. We tested the algorithm on mobile-LiDAR-scanned roadside trees and T-LiDAR-scanned broadleaved and coniferous forests in China. Point-level quantitative assessments of the segmentation results showed that for mobile-LiDAR-scanned roadside trees, all the points were classified to their corresponding trees correctly, and for T-LiDAR-scanned broadleaved and coniferous forests, kappa coefficients ranging from 0.83 to 0.93 were obtained. We believe that our algorithm will make a contribution to solving the problem of crown segmentation in T-LiDAR scanned-forests, and might be of interest to researchers in LiDAR data processing and to forest ecologists. In addition, our research highlights the advantages of using ecological theories as guidelines for processing LiDAR data. [ABSTRACT FROM AUTHOR]

High-resolution satellite imagery (HRSI) and airborne light detection and ranging (LiDAR) data are widely used for deriving 3D spatial information. However, the 3D spatial information derived from them in the same area can be inconsistent. Considering HRSI and LiDAR datasets taken from metropolitan areas as a case study, this paper presents a novel approach to the geometric integration of HRSI and LiDAR data to reduce their inconsistencies and improve their geopositioning accuracy. First, the influences of HRSI’s individual rational polynomial coefficients (RPCs) on geopositioning accuracies are analyzed and the RPCs that dominate those accuracies are identified. The RPCs are then used as inputs in the geometric integration model together with the tie points identified in stereo images and LiDAR ground points. A local vertical constraint and a local horizontal constraint are also incorporated in the model to ensure vertical and horizontal consistency between the two datasets. The model improves the dominating RPCs and the ground coordinates of the LiDAR points, decreasing the inconsistencies between the two datasets and improving their geopositioning accuracy. Experiments were conducted using ZY-3 and Pleiades-1 imagery and the corresponding airborne LiDAR data in Hong Kong. The results verify that the geometric integration model effectively improves the geopositioning accuracies of both types of imagery and the LiDAR points. Furthermore, the model enables the full comparative and synergistic use of remote sensing imagery and laser scanning data collected from different platforms and sensors. [ABSTRACT FROM AUTHOR]

Current research into the fusion of hyperspectral imagery (HI) and full waveform LiDAR (Light Detection And Ranging) has relied on first processing the full waveform LiDAR (FWL) data to a set of discrete returns before merging because the data structure and sampling interval of HI and FWL are distinctly different. However, additional information about target properties can potentially be recovered if the waveform shape is preserved in the fusion process. This paper proposes a “voxelization” method to register FWL data to HI by dividing the waveform data into voxels, and then synthesizing all waveforms which intersect a voxel column into one three-dimensional superposition waveform: the synthesized waveform (SWF). A vertical energy distribution coefficients (VEDC) feature is proposed for extracting features from SWF, and then the SWF and HI are fused to form a complete feature space for classification. A pairwise classifier was adapted and completed using both Maximum Likelihood and Support Vector Machine classifiers for the combined SWF/HI features. Results show that this method of generating SWF from FWL data can effectively preserve information from the original waveforms, and the fusion of SWF and HI enhanced land cover classification compared to both using either data set alone or the merging of HI with a discrete LiDAR return point cloud. [ABSTRACT FROM AUTHOR]

Light Detection and Ranging (LiDAR) data is being increasingly used as an effective alternative to conventional optical remote sensing to accurately estimate aboveground forest biomass ranging from individual tree to stand levels. Recent advancements in LiDAR technology have resulted in higher point densities and improved data accuracies accompanied by challenges for procuring and processing voluminous LiDAR data for large-area assessments. Reducing point density lowers data acquisition costs and overcomes computational challenges for large-area forest assessments. However, how does lower point density impact the accuracy of biomass estimation in forests containing a great level of anthropogenic disturbance? We evaluate the effects of LiDAR point density on the biomass estimation of remnant forests in the rapidly urbanizing region of Charlotte, North Carolina, USA. We used multiple linear regression to establish a statistical relationship between field-measured biomass and predictor variables derived from LiDAR data with varying densities. We compared the estimation accuracies between a general Urban Forest type and three Forest Type models (evergreen, deciduous, and mixed) and quantified the degree to which landscape context influenced biomass estimation. The explained biomass variance of the Urban Forest model, using adjusted R 2 , was consistent across the reduced point densities, with the highest difference of 11.5% between the 100% and 1% point densities. The combined estimates of Forest Type biomass models outperformed the Urban Forest models at the representative point densities (100% and 40%). The Urban Forest biomass model with development density of 125 m radius produced the highest adjusted R 2 (0.83 and 0.82 at 100% and 40% LiDAR point densities, respectively) and the lowest RMSE values, highlighting a distance impact of development on biomass estimation. Our evaluation suggests that reducing LiDAR point density is a viable solution to regional-scale forest assessment without compromising the accuracy of biomass estimates, and these estimates can be further improved using development density. [ABSTRACT FROM AUTHOR]

Large footprint waveform LiDAR sensors have been widely used for numerous airborne studies. Ground peak identification in a large footprint waveform is a significant bottleneck in exploring full usage of the waveform datasets. In the current study, an accurate and computationally efficient algorithm was developed for ground peak identification, called Filtering and Clustering Algorithm (FICA). The method was evaluated on Land, Vegetation, and Ice Sensor (LVIS) waveform datasets acquired over Central NY. FICA incorporates a set of multi-scale second derivative filters and a k-means clustering algorithm in order to avoid detecting false ground peaks. FICA was tested in five different land cover types (deciduous trees, coniferous trees, shrub, grass and developed area) and showed more accurate results when compared to existing algorithms. More specifically, compared with Gaussian decomposition, the RMSE ground peak identification by FICA was 2.82 m (5.29 m for GD) in deciduous plots, 3.25 m (4.57 m for GD) in coniferous plots, 2.63 m (2.83 m for GD) in shrub plots, 0.82 m (0.93 m for GD) in grass plots, and 0.70 m (0.51 m for GD) in plots of developed areas. FICA performance was also relatively consistent under various slope and canopy coverage (CC) conditions. In addition, FICA showed better computational efficiency compared to existing methods. FICA's major computational and accuracy advantage is a result of the adopted multi-scale signal processing procedures that concentrate on local portions of the signal as opposed to the Gaussian decomposition that uses a curve-fitting strategy applied in the entire signal. The FICA algorithm is a good candidate for large-scale implementation on future space-borne waveform LiDAR sensors. [ABSTRACT FROM AUTHOR]

Abstract: There are two main challenges when it comes to classifying airborne laser scanning (ALS) data. The first challenge is to find suitable attributes to distinguish classes of interest. The second is to define proper entities to calculate the attributes. In most cases, efforts are made to find suitable attributes and less attention is paid to defining an entity. It is our hypothesis that, with the same defined attributes and classifier, accuracy will improve if multiple entities are used for classification. To verify this hypothesis, we propose a multiple-entity based classification method to classify seven classes: ground, water, vegetation, roof, wall, roof element, and undefined object. We also compared the performance of the multiple-entity based method to the single-entity based method. Features have been extracted, in most previous work, from a single entity in ALS data; either from a point or from grouped points. In our method, we extract features from three different entities: points, planar segments, and segments derived by mean shift. Features extracted from these entities are inputted into a four-step classification strategy. After ALS data are filtered into ground and non-ground points. Features generalised from planar segments are used to classify points into the following: water, ground, roof, vegetation, and undefined objects. This is followed by point-wise identification of the walls and roof elements using the contextual information of a building. During the contextual reasoning, the portion of the vegetation extending above the roofs is classified as a roof element. This portion of points is eventually re-segmented by the mean shift method and then reclassified. Five supervised classifiers are applied to classify the features extracted from planar segments and mean shift segments. The experiments demonstrate that a multiple-entity strategy achieves slightly higher overall accuracy and achieves much higher accuracy for vegetation, in comparison to the single-entity strategy (using only point features and planar segment features). Although the multiple-entity method obtains nearly the same overall accuracy as the planar-segment method, the accuracy of vegetation improves by 3.3% with the rule-based classifier. The multiple-entity method obtains much higher overall accuracy and higher accuracy in vegetation in comparison to using only the point-wise classification method for all five classifiers. Meanwhile, we compared the performances of five classifiers. The rule-based method provides the highest overall accuracy at 97.0%. The rule-based method provides over 99.0% accuracy for the ground and roof classes, and a minimum accuracy of 90.0% for the water, vegetation, wall and undefined object classes. Notably, the accuracy of the roof element class is only 70% with the rule-based method, or even lower with other classifiers. Most roof elements have been assigned to the roof class, as shown in the confusion matrix. These erroneous assignments are not fatal errors because both a roof and a roof element are part of a building. In addition, a new feature which indicates the average point space within the planar segment is generalised to distinguish vegetation from other classes. Its performance is compared to the percentage of points with multiple pulse count in planar segments. Using the feature computed with only average point space, the detection rate of vegetation in a rule-based classifier is 85.5%, which is 6% lower than that with pulse count information. [Copyright &y& Elsevier]

Abstract: Light Detection and Ranging (lidar) has become a valuable tool in forest inventory because it yields accurate measurements of tree heights. However, tree height can be accurate only if the height of the ground, i. e., the Digital Terrain Model (dtm) is first accurately established. Although great advances have been made in lidar technology over the past decade, filtering lidar data for Digital Terrain Model (dtm) interpolation is still a challenge, especially in steep and complex terrain with forest cover. Several algorithms proposed in the literature address this challenge but their performance deteriorates with the decreasing point density caused by the presence of forest cover and steep slopes. In this paper, we propose a new adaptive algorithm for dtm interpolation from lidar data in steep terrain with forest cover. The algorithm partitions the input data and estimates a section of the dtm by fitting a linear or quadratic trend surface, or uses cubic spline interpolation depending on the complexity of the section of terrain. The performance of the algorithm is tested in three ways: by visual assessment, by comparison of the tree-height estimates produced using the generated dtm with those obtained using field survey, and by use of International Society for Photogrammetry and Remote Sensing (isprs) test data. Test results show that the algorithm can cope well with steep slopes and low lidar point densities, giving a more accurate estimate of average tree height compared to conventional algorithms. The algorithm can be used for dtm extraction in large scale forest inventory projects in challenging environments–complex terrain and low lidar point densities. [Copyright &y& Elsevier]

Abstract: Terrain classification of LIDAR point clouds is a fundamental problem in the production of Digital Elevation Models (DEMs). The Simple Morphological Filter (SMRF) addresses this problem by applying image processing techniques to the data. This implementation uses a linearly increasing window and simple slope thresholding, along with a novel application of image inpainting techniques. When tested against the ISPRS LIDAR reference dataset, SMRF achieved a mean 85.4% Kappa score when using a single parameter set and 90.02% when optimized. SMRF is intended to serve as a stable base from which more advanced progressive filters can be designed. This approach is particularly effective at minimizing Type I error rates, while maintaining acceptable Type II error rates. As a result, the final surface preserves subtle surface variation in the form of tracks and trails that make this approach ideally suited for the production of DEMs used as ground surfaces in immersive virtual environments. [Copyright &y& Elsevier]

Abstract: This article presents a novel, fully automatic method for the reconstruction of three-dimensional building models with prototypical roofs (CityGML LoD2) from LIDAR data and building footprints. The proposed method derives accurate results from sparse point data sets and is suitable for large area reconstruction. Sparse LIDAR data are widely available nowadays. Robust estimation methods such as RANSAC/MSAC, are applied to derive best fitting roof models in a model-driven way. For the identification of the most probable roof model, supervised machine learning methods (Support Vector Machines) are used. In contrast to standard approaches (where the best model is selected via MDL or AIC), supervised classification is able to incorporate additional features enabling a significant improvement in model selection accuracy. [Copyright &y& Elsevier]

Abstract: Airborne laser scanning LiDAR systems deliver not only geometric (X, Y, Z) information of the scanned surfaces but also the returned intensity of the laser pulse. Recent studies have shown the potential of using intensity data for many applications. However, there are limitations in using the raw intensity data because of radiometric system bias, reflectance noise and variations between adjacent strips. To overcome these limitations, a three-step LiDAR intensity correction algorithm is proposed. Following corrections for environmental and surface effects, an overlap-driven least-squares adjustment model that does not rely on the selection of homologous points minimizes intensity differences in the overlap area of strips. Finally, the Phong reflection model, which describes both diffuse and specular reflectance, is used to attenuate the effects of strong reflections that typically occur over wet or water dominated areas. The algorithm was applied to a multi-strip LiDAR dataset that covers wetlands in the estuary of the Yellow River, People’s Republic of China. Results demonstrated a significant reduction in radiometric differences in the overlap areas, and strong specular reflections in the nadir regions were reduced. Objects which were obscured by the specular reflection in the original intensity data were clearly identifiable after the adjustment. [Copyright &y& Elsevier]

Abstract: The structural characteristics of Light Detection and Ranging (LiDAR) data are increasingly used to classify urban environments at fine scales, but have been underutilized for distinguishing heterogeneous land covers over large urban regions due to high cost, limited spectral information, and the computational difficulties posed by inherently large data volumes. Here we explore tradeoffs between potential gains in mapping accuracy with computational costs by integrating structural and intensity surface models extracted from LiDAR data with Landsat Thematic Mapper (TM) imagery and evaluating the degree to which TM, LiDAR, and LiDAR-TM fusion data discriminated land covers in the rapidly urbanizing region of Charlotte, North Carolina, USA. Using supervised maximum likelihood (ML) and classification tree (CT) methods, we classified TM data at 30m and LiDAR data and LiDAR-TM fusions at 1m, 5m, 10m, 15m and 30m resolutions. We assessed the relative contributions of LiDAR structural and intensity surface models to classification map accuracy and identified optimal spatial resolution of LiDAR surface models for large-area assessments of urban land cover. ML classification of 1m LiDAR-TM fusions using both structural and intensity surface models increased total accuracy by 32% compared to LiDAR alone and by 8% over TM at 30m. Fusion data using all LiDAR surface models improved class discrimination of spectrally similar forest, farmland, and managed clearings and produced the highest total accuracies at 1m, 5m, and 10m resolutions (87.2%, 86.3% and 85.4%, respectively). At all resolutions of fusion data and using either ML or CT classifier, the relative contribution of the LiDAR structural surface models (canopy height and normalized digital surface model) to classification accuracy is greater than the intensity surface. Our evaluation of tradeoffs between data volume and thematic map accuracy for this study system suggests that a spatial resolution of 5m for LiDAR surface models best balances classification performance and the computational challenges posed by large-area assessments of land cover. [Copyright &y& Elsevier]

Abstract: This paper investigates the problem of latency estimation between an IMU (Inertial Measurement Unit) and a LiDAR (Light Detection And Ranging). The latency is due to the IMU itself, but also to the acquisition software and hardware configuration, which is generally set-up by survey systems users. We propose a method for latency estimation, and we show that this method meets the accuracy requirements of most LiDAR survey applications. We present test results of our method on various acquisition systems and hardware configuration which demonstrate that it is able to identify very accurately the total IMU–LiDAR latency through a simple procedure. The principle of the method is to put the LiDAR–IMU in rotational motions, thanks to a rotating table. By scanning a spherical target at different angular velocities, we can observe position shifts of the target center from which we derive an estimate the IMU–LiDAR latency. The method we propose works without absolute positioning and is therefore not sensitive to nonmodeled errors coming from GPS geolocated data. We show that in estimating accurately the LiDAR–IMU latency, we can optimize the configuration of a mobile LiDAR survey system in order to enhance its robustness with respect to high motion dynamics of the survey platform. [Copyright &y& Elsevier]

Abstract: We used a high-resolution portable scanning lidar together with a lightweight mirror and a voxel-based canopy profiling method to estimate the vertical plant area density (PAD) profile of a rice (Oryza sativa L. cv. Koshihikari) canopy at different growth stages. To improve the laser’s penetration of the dense canopy, we used a mirror to change the direction of the laser beam from horizontal to vertical (0°) and off-vertical (30°). The estimates of PAD and plant area index (PAI) were more accurate at 30° than at 0°. The root-mean-square errors of PAD at each growth stage ranged from 1.04 to 3.33m2 m−3 at 0° and from 0.42 to 2.36m2 m−3 at 30°, and those across all growth stages averaged 1.79m2 m−3 at 0° and 1.52m2 m−3 at 30°. The absolute percent errors of PAI at each growth stage ranged from 1.8% to 66.1% at 0° and from 4.3% to 23.2% at 30°, and those across all growth stages averaged 30.4% at 0° and 14.8% at 30°. The degree of laser beam coverage of the canopy (expressed as index Ω) explained these errors. From the estimates of PAD at 30°, regressions between the areas of stems, leaves, and ears per unit ground area and actual dry weights gave standard errors of 7.9gm−2 for ears and 12.2gm−2 for stems and leaves. [Copyright &y& Elsevier]