Search Results (1,608)

Old-growth forests are essential for maintaining biodiversity, as they are formed by the complexity of diverse forest structures, such as broad variations in tree height and diameter (DBH) and conditions of living and dead trees, leading to various ecological niches. However, many efforts of old-growth forest mapping from LiDAR have targeted only one specific forest structure (e.g., stand height, basal area, or stand density) by deriving information through a large number of LiDAR metrics. This study introduces a novel approach for identifying old-growth forests by optimizing a set of selected LiDAR standards and structural metrics. These metrics effectively capture the arrangement of multiple forest structures, such as canopy heterogeneity, multilayer canopy profile, and canopy openness. To determine the important LiDAR standard and structural metrics in identifying old-growth forests, multicollinearity analysis using the variance inflation factor (VIF) approach was applied to identify and remove metrics with high collinearity, followed by the random forest algorithm to rank which LiDAR standard and structural metrics are important in old-growth forest classification. The results demonstrate that the LiDAR structural metrics (i.e., advanced LiDAR metrics related to multiple canopy structures) are more important and effective in distinguishing old- and second-growth forests than LiDAR standard metrics (i.e., height- and density-based LiDAR metrics) using the European definition of a 150-year stand age threshold for old-growth forests. These structural metrics were then used as predictors for the final classification of old-growth forests, yielding an overall accuracy of 78%, with a true skill statistic (TSS) of 0.58 for the test dataset. This study demonstrates that using a few structural LiDAR metrics provides more information than a high number of standard LiDAR metrics, particularly for identifying old-growth forests in mixed temperate forests. The findings can aid forest and national park managers in developing a practical and efficient old-growth forest identification and monitoring method using LiDAR. Full article

Reduced-impact logging (RIL) has been recognized as a promising strategy for biodiversity conservation and carbon sequestration within sustainable forest management (SFM) areas. However, monitoring the forest understory—a critical area for assessing logging impacts—remains challenging due to limitations in conventional methods such as field inventories and global navigation satellite system (GNSS) surveys, which are time-consuming, costly, and often lack accuracy in complex environments. Additionally, aerial and satellite imagery frequently underestimate the full extent of disturbances as the forest canopy obscures understory impacts. This study examines the effectiveness of the relative density model (RDM), derived from airborne LiDAR data, for mapping and monitoring understory disturbances. A field-based validation of LiDAR-derived RDM was conducted across 25 sites, totaling 5504.5 hectares within the Jamari National Forest, Rondônia, Brazil. The results indicate that the RDM accurately delineates disturbances caused by logging infrastructure, with over 90% agreement with GNSS field data. However, the model showed the greatest discrepancy for skid trails, which, despite their lower accuracy in modeling, accounted for the largest proportion of the total impacted area among infrastructure. The findings include the mapping of 35.1 km of primary roads, 117.4 km of secondary roads, 595.6 km of skid trails, and 323 log landings, with skid trails comprising the largest proportion of area occupied by logging infrastructure. It is recommended that airborne LiDAR assessments be conducted up to two years post-logging, as impacts become less detectable over time. This study highlights LiDAR data as a reliable alternative to traditional monitoring approaches, with the ability to detect understory impacts more comprehensively for monitoring selective logging in SFM areas of the Amazon, providing a valuable tool for both conservation and climate mitigation efforts. Full article

Forest canopy height (FCH) is a critical parameter for forest management and ecosystem modeling, but there is a lack of accurate FCH distribution in large areas. To address this issue, this study selected Wuyishan National Park in China as a case study to explore the calibration method for mapping FCH in a complex subtropical mountainous region based on ZiYuan-3 (ZY3) stereo imagery and limited Unmanned Aerial Vehicle (UAV) LiDAR data. Pearson’s correlation analysis, Categorical Boosting (CatBoost) feature importance analysis, and causal effect analysis were used to examine major factors causing extraction errors of digital surface model (DSM) data from ZY3 stereo imagery. Different machine learning algorithms were compared and used to calibrate the DSM and FCH results. The results indicate that the DSM extraction accuracy based on ZY3 stereo imagery is primarily influenced by slope aspect, elevation, and vegetation characteristics. These influences were particularly notable in areas with a complex topography and dense vegetation coverage. A Bayesian-optimized CatBoost model with directly calibrating the original FCH (the difference between the DSM from ZY3 and high-precision digital elevation model (DEM) data) demonstrated the best prediction performance. This model produced the FCH map at a 4 m spatial resolution, the root mean square error (RMSE) was reduced from 6.47 m based on initial stereo imagery to 3.99 m after calibration, and the relative RMSE (rRMSE) was reduced from 36.52% to 22.53%. The study demonstrates the feasibility of using ZY3 imagery for regional forest canopy height mapping and confirms the superior performance of using the CatBoost algorithm in enhancing FCH calibration accuracy. These findings provide valuable insights into the multidimensional impacts of key environmental factors on FCH extraction, supporting precise forest monitoring and carbon stock assessment in complex terrains in subtropical regions. Full article

The ability to segment moving objects from three-dimensional (3D) LiDAR scans is critical to advancing autonomous driving technology, facilitating core tasks like localization, collision avoidance, and path planning. In this paper, we introduce a novel deep neural network designed to enhance the performance of 3D LiDAR point cloud moving object segmentation (MOS) through the integration of image gradient information and the principle of motion consistency. Our method processes sequential range images, employing depth pixel difference convolution (DPDC) to improve the efficacy of dilated convolutions, thus boosting spatial information extraction from range images. Additionally, we incorporate Bayesian filtering to impose posterior constraints on predictions, enhancing the accuracy of motion segmentation. To handle the issue of uneven object scales in range images, we develop a novel edge-aware loss function and use a progressive training strategy to further boost performance. Our method is validated on the SemanticKITTI-based LiDAR MOS benchmark, where it significantly outperforms current state-of-the-art (SOTA) methods, all while working directly on two-dimensional (2D) range images without requiring mapping. Full article

Various nutrients are needed during the sugarcane growing season for plant development and productivity. However, traditional methods for assessing nutritional status are often costly and time consuming. This study aimed to determine the level of nitrogen (N), phosphorus (P), potassium (K) and chlorophyll of sugarcane plants using remote sensing. Remotely sensed images were obtained using a MicaSense RedEdge-P camera attached to a drone. Leaf chlorophyll content was measured in the field using an N-Tester chlorophyll meter, and leaf samples were collected and analyzed in the laboratory for N, P and K. The highest correlation between field samples and predictor variables (spectral bands, selected vegetation indices, and plant height from Light Detection and Ranging (LiDAR)), were noted.The spatial distribution of chlorophyll, N, P, and K maps achieved 60%, 75%, 96% and 50% accuracies, respectively. The spectral profiles helped to identify areas with visual differences. Spatial variability of nutrient maps confirmed that moisture presence leads to nitrogen and potassium deficiencies, excess phosphorus, and a reduction in vegetation density (93.82%) and height (2.09 m), compared to green, healthy vegetation (97.64% density and 3.11 m in height). This robust method of assessing foliar nutrients is repeatable for the same sugarcane variety at certain conditions and leads to sustainable agricultural practices in Costa Rica. Full article

The key to building a 3D point cloud map is to ensure the consistency and accuracy of point cloud data. However, the hardware limitations of LiDAR lead to a sparse and uneven distribution of point cloud data in the edge region, which brings many challenges to 3D map construction, such as low registration accuracy and high construction errors in the sparse regions. To solve these problems, this paper proposes the I-PAttnGAN network to generate point clouds with image-assisted approaches, which aims to improve the density and uniformity of sparse regions and enhance the representation ability of point cloud data in sparse edge regions for distant objects. I-PAttnGAN uses the normalized flow model to extract the point cloud attention weights dynamically and then integrates the point cloud weights into image features to learn the transformation relationship between the weighted image features and the point cloud distribution, so as to realize the adaptive generation of the point cloud density and resolution. Extensive experiments are conducted on ShapeNet and nuScenes datasets. The results show that I-PAttnGAN significantly improves the performance of generating high-quality, dense point clouds in low-density regions compared with existing methods: the Chamfer distance value is reduced by about 2 times, the Earth Mover’s distance value is increased by 1.3 times, and the F1 value is increased by about 1.5 times. In addition, the effectiveness of the newly added modules is verified by ablation experiments, and the experimental results show that these modules play a key role in the generation process. Overall, the proposed model shows significant advantages in terms of accuracy and efficiency, especially in generating complete spatial point clouds. Full article

In this paper, the application is investigated of fast Fourier transform filtering (FFT-FR) to high spatial resolution digital terrain models (HR-DTM) derived from LiDAR sensors, assessing its efficacy in identifying genuine relief elements, including both natural geological features and anthropogenic landforms. The suitability of the derived filtered geomorphic references (FGRs) is evaluated through spatial correlation with ground truths (GTs) extracted from the topographical and geological geodatabases of Santander Bay, Northern Spain. In this study, it is revealed that existing artefacts, derived from vegetation or human infrastructures, pose challenges in the units’ construction, and large physiographic units are better represented using low-pass filters, whereas detailed units are more accurately depicted with high-pass filters. The results indicate a propensity of high-frequency filters to detect anthropogenic elements within the DTM. The quality of GTs used for validation proves more critical than the geodatabase scale. Additionally, in this study, it is demonstrated that the footprint of buildings remains uneliminated, indicating that the model is a poorly refined digital surface model (DSM) rather than a true digital terrain model (DTM). Experiments validate the DTM’s capability to highlight contacts and constructions, with water detection showing high precision (≥60%) and varying precision for buildings. Large units are better captured with low filters, whilst high filters effectively detect anthropogenic elements and more detailed units. This facilitates the design of validation and correction procedures for DEMs derived from LiDAR point clouds, enhancing the potential for more accurate and objective Earth surface representation. Full article

Hyperspectral image (HSI) and light detection and ranging (LiDAR) data joint classification has been applied in the field of ground category recognition. However, existing methods still perform poorly in extracting high-dimensional features and elevation information, resulting in insufficient data classification accuracy. To address this challenge, we propose a novel and efficient Calibration-Enhanced Multi-Awareness Network (CEMA-Net), which exploits the joint spectral–spatial–elevation features in depth to realize the accurate identification of land cover categories. Specifically, we propose a novel multi-way feature retention (MFR) module that explores deep spectral–spatial–elevation semantic information in the data through multiple paths. In addition, we propose spectral–spatial-aware enhancement (SAE) and elevation-aware enhancement (EAE) modules, which effectively enhance the awareness of ground objects that are sensitive to spectral and elevation information. Furthermore, to address the significant representation disparities and spatial misalignments between multi-source features, we propose a spectral–spatial–elevation feature calibration fusion (SFCF) module to efficiently integrate complementary characteristics from heterogeneous features. It incorporates two key advantages: (1) efficient learning of discriminative features from multi-source data, and (2) adaptive calibration of spatial differences. Comparative experimental results on the MUUFL, Trento, and Augsburg datasets demonstrate that CEMA-Net outperforms existing state-of-the-art methods, achieving superior classification accuracy with better feature map precision and minimal noise. Full article

This study presents an adaptation of the YOLOv4 deep learning algorithm for 3D object detection, addressing a critical challenge in autonomous vehicle (AV) systems: accurate real-time perception of the surrounding environment in three dimensions. Traditional 2D detection methods, while efficient, fall short in providing the depth and spatial information necessary for safe navigation. This research modifies the YOLOv4 architecture to predict 3D bounding boxes, object depth, and orientation. Key contributions include introducing a multi-task loss function that optimizes 2D and 3D predictions and integrating sensor fusion techniques that combine RGB camera data with LIDAR point clouds for improved depth estimation. The adapted model, tested on real-world datasets, demonstrates a significant increase in 3D detection accuracy, achieving a mean average precision (mAP) of 85%, intersection over union (IoU) of 78%, and near real-time performance at 93–97% for detecting vehicles and 75–91% for detecting people. This approach balances high detection accuracy and real-time processing, making it highly suitable for AV applications. This study advances the field by showing how an efficient 2D detector can be extended to meet the complex demands of 3D object detection in real-world driving scenarios without sacrificing computational efficiency. Full article

Accurate estimates of forest aboveground biomass (AGB) are necessary for the accurate tracking of forest carbon stock. Gaofen-7 (GF-7) is the first civilian sub-meter three-dimensional (3D) mapping satellite from China. It is equipped with a laser altimeter system and a dual-line array stereoscopic mapping camera, which enables it to synchronously generate full-waveform LiDAR data and stereoscopic images. The bulk of existing research has examined how accurate GF-7 is for topographic measurements of bare land or canopy height. The measurement of forest aboveground biomass has not received as much attention as it deserves. This study aimed to assess the GF-7 stereo imaging capability, displayed as topographic features for aboveground biomass estimation in forests. The aboveground biomass model was constructed using the random forest machine learning technique, which was accomplished by combining the use of in situ field measurements, pairs of GF-7 stereo images, and the corresponding generated canopy height model (CHM). Findings showed that the biomass estimation model had an accuracy of R² = 0.76, RMSE = 7.94 t/ha, which was better than the inclusion of forest canopy height (R² = 0.30, RMSE = 21.02 t/ha). These results show that GF-7 has considerable application potential in gathering large-scale high-precision forest aboveground biomass using a restricted amount of field data. Full article

Reconfigurable Intelligent Surface (RIS) panels have garnered significant attention with the emergence of next-generation network technologies. This paper proposes a novel data-driven approach that leverages Light Detecting and Ranging (LiDAR) sensors to enhance user localization and beamforming in RIS-assisted networks. Integrating LiDAR sensors into the network will be instrumental, offering high-speed and precise 3D mapping capabilities, even in low light or adverse weather conditions. LiDAR data facilitate user localization, enabling the determination of optimal RIS coefficients. Our approach extends a Graph Neural Network (GNN) by integrating LiDAR-captured user locations as inputs. This extension enables the GNN to effectively learn the mapping from received pilots to optimal beamformers and reflection coefficients to maximize the RIS-assisted sumrate among multiple users. The permutation-equivariant and -invariant properties of the GNN proved advantageous in efficiently handling the LiDAR data. Our simulation results demonstrated significant improvements in sum rates compared with conventional methods. Specifically, including locations improved on excluding locations by up to 25% and outperformed the Linear Minimum Mean Squared Error (LMMSE) channel estimation by up to 85% with varying downlink power and 98% with varying pilot lengths, and showed a remarkable 190% increase with varying downlink power compared with scenarios excluding the RIS. Full article

Environmental perception is crucial for safe autonomous driving, enabling accurate analysis of the vehicle’s surroundings. While 3D LiDAR is traditionally used for 3D environment reconstruction, its high cost and complexity present challenges. In contrast, camera-based cross-view frameworks can offer a cost-effective alternative. Hence, this manuscript proposes a new cross-view model to extract mapping features from camera images and then transfer them to a Bird’s-Eye View (BEV) map. Particularly, a multi-head attention mechanism in the decoder architecture generates the final semantic map. Each camera learns embedding information corresponding to its position and angle within the BEV map. Cross-view attention fuses information from different perspectives to predict top-down map features enriched with spatial information. The multi-head attention mechanism then globally performs dependency matches, enhancing long-range information and capturing latent relationships between features. Transposed convolution replaces traditional upsampling methods, avoiding high similarities of local features and facilitating semantic segmentation inference of the BEV map. Finally, we conduct numerous simulation experiments to verify the performance of our cross-view model. Full article

To address the shortcomings of light detection and ranging (LiDAR) sensors in extracting road surface elevation information in front of a vehicle, a scheme for digital terrain construction based on the fusion of an Inertial Measurement Unit (IMU) and LiDAR perception is proposed. First, two sets of sensor coordinate systems were configured, and the parameters of LiDAR and IMU were calibrated. Then, a terrain construction system based on the fusion perception of IMU and LiDAR was established, and improvements were made to the state estimation and mapping architecture. Terrain construction experiments were conducted in an academic setting. Finally, based on the output information from the terrain construction system, a moving average-like algorithm was designed to process point cloud data and extract the road surface elevation information at the vehicle’s trajectory position. By comparing the extraction effects of four different sliding window widths, the 4 cm width sliding window, which yielded the best results, was ultimately selected, making the extracted road surface elevation information more accurate and effective. Full article

Handheld LiDAR scanners, which typically consist of a LiDAR sensor, Inertial Measurement Unit, and processor, enable data capture while moving, offering flexibility for various applications, including indoor and outdoor 3D mapping in fields such as architecture and civil engineering. Unlike fixed LiDAR systems, handheld devices allow data collection from different angles, but this mobility introduces challenges in data quality, particularly when initial calibration between sensors is not precise. Accurate LiDAR-IMU calibration, essential for mapping accuracy in Simultaneous Localization and Mapping applications, involves precise alignment of the sensors’ extrinsic parameters. This research presents a robust initial pose calibration method for LiDAR-IMU systems in handheld devices, specifically designed for indoor environments. The research contributions are twofold. Firstly, we present a robust plane detection method for LiDAR data. This plane detection method removes the noise caused by mobility of scanning device and provides accurate planes for precise LiDAR initial pose estimation. Secondly, we present a robust planes-aided LiDAR calibration method that estimates the initial pose. By employing this LiDAR calibration method, an efficient LiDAR-IMU calibration is achieved for accurate mapping. Experimental results demonstrate that the proposed method achieves lower calibration errors and improved computational efficiency compared to existing methods. Full article

Forest types correspond to differences in structural characteristics and species composition that influence biomass and biodiversity values, which are essential measurements for ecological monitoring and management. However, differentiating forest types in tropical regions remains a challenge. This study aimed to improve forest type extent mapping by combining structural information from discrete full-waveform LiDAR returns with multitemporal images. This study was conducted in a tropical forest region over complex terrain in north-eastern Tanzania. First, structural classes were generated by applying time-series clustering algorithms. The results showed four different structural clusters corresponding to forest types, montane–humid forest, montane–dry forest, submontane forest, and non-forest, when using the Kshape algorithm. Kshape considers the shape of the full-sequence LiDAR waveform, requiring little preprocessing. Despite the overlap amongst the original clusters, the averages of structural characteristics were significantly different across all but five metrics. The labeled clusters were then further refined and used as training data to generate a wall-to-wall forest cover type map by classifying biannual images. The highest-performing model was a KNN model with 13 spectral and 3 terrain features achieving 81.7% accuracy. The patterns in the distributions of forest types provide better information from which to adapt forest management, particularly in forest–non-forest transitional zones. Full article