Light Detection And Ranging Data Research Articles

Image-Aided LiDAR Extraction, Classification, and Characterization of Lane Markings from Mobile Mapping Data

The documentation of roadway factors (such as roadway geometry, lane marking retroreflectivity/classification, and lane width) through the inventory of lane markings can reduce accidents and facilitate road safety analyses. Typically, lane marking inventory is established using either imagery or Light Detection and Ranging (LiDAR) data collected by mobile mapping systems (MMS). However, it is important to consider the strengths and weaknesses of both camera and LiDAR units when establishing lane marking inventory. Images may be susceptible to weather and lighting conditions, and lane marking might be obstructed by neighboring traffic. They also lack 3D and intensity information, although color information is available. On the other hand, LiDAR data are not affected by adverse weather and lighting conditions, and they have minimal occlusions. Moreover, LiDAR data provide 3D and intensity information. Considering the complementary characteristics of camera and LiDAR units, an image-aided LiDAR framework would be highly advantageous for lane marking inventory. In this context, an image-aided LiDAR framework means that the lane markings generated from one modality (i.e., either an image or LiDAR) are enhanced by those derived from the other one (i.e., either imagery or LiDAR). In addition, a reporting mechanism that can handle multi-modal datasets from different MMS sensors is necessary for the visualization of inventory results. This study proposes an image-aided LiDAR lane marking inventory framework that can handle up to five lanes per driving direction, as well as multiple imaging and LiDAR sensors onboard an MMS. The framework utilizes lane markings extracted from images to improve LiDAR-based extraction. Thereafter, intensity profiles and lane width estimates can be derived using the image-aided LiDAR lane markings. Finally, imagery/LiDAR data, intensity profiles, and lane width estimates can be visualized through a web portal that has been developed in this study. For the performance evaluation of the proposed framework, lane markings obtained through LiDAR-based, image-based, and image-aided LiDAR approaches are compared against manually established ones. The evaluation demonstrates that the proposed framework effectively compensates for the omission errors in the LiDAR-based extraction, as evidenced by an increase in the recall from 87.6% to 91.6%.

Combining LiDAR and Spaceborne Multispectral Data for Mapping Successional Forest Stages in Subtropical Forests

The Brazilian Atlantic Rainforest presents great diversity of flora and stand structures, making it difficult for traditional forest inventories to collect reliable and recurrent information to classify forest succession stages. In recent years, remote sensing data have been explored to save time and effort in classifying successional forest stages. However, there is a need to understand if any of these sensors stand out for this purpose. Here, we evaluate the use of multispectral satellite data from four different platforms (CBERS-4A, Landsat-8/OLI, PlanetScope, and Sentinel-2) and airborne light detection and ranging (LiDAR) to classify three forest succession stages in a subtropical ombrophilous mixed forest located in southern Brazil. Different features extracted from multispectral and LiDAR data, such as spectral bands, vegetation indices, texture features, and the canopy height model (CHM) and LiDAR intensity, were explored using two conventional machine learning methods such as random trees (RT) and support vector machine (SVM). The statistically based maximum likelihood (MLC) algorithm was also compared. The classification accuracy was evaluated by generating a confusion matrix and calculating the kappa index and standard deviation based on field measurements and unmanned aerial vehicle (UAV) data. Our results show that the kappa index ranged from 0.48 to 0.95, depending on the chosen dataset and method. The best result was obtained using the SVM algorithm associated with spectral bands, CHM, LiDAR intensity, and vegetation indices, regardless of the sensor. Datasets with Landsat-8 or Sentinel-2 information performed better results than other optical sensors, which may be due to the higher intraclass variability and less spectral bands in CBERS-4A and PlanetScope data. We found that the height information derived from airborne LiDAR and its intensity combined with the multispectral data increased the classification accuracy. However, the results were also satisfactory when using only multispectral data. These results highlight the potential of using freely available satellite information and open-source software to optimize forest inventories and monitoring, enabling a better understanding of forest structure and potentially supporting forest management initiatives and environmental licensing programs.

MAPPING 3D STRUCTURE OF URBAN TREES USING AIRBORNE LIDAR DATA FOR TOPOGRAPHIC SURVEY

Abstract. The designing of urban spaces utilizing trees is a promising approach to alleviate the severe thermal environment. Three-dimensional (3D) information on individual trees is important for their maintenance and for evaluating their microclimatic effects. Methods have been developed for estimating the 3D distribution of leaf area density (LAD) using high-density airborne light detection and ranging (LiDAR) point cloud. However, the coverage of high-density data is limited. This study sought to develop a method for estimating LAD distribution using existing low-density LiDAR data that was obtained for the nationwide topographic survey. A point cloud with the same density as the existing data was generated from the high-density data. LAD was estimated using the generated point cloud and was compared with LAD derived from the high-density data and terrestrial LiDAR to clarify the spatial scale of information obtained from the existing data. A method to improve the spatial scale was also examined. These methods were applied to the actual existing LiDAR data to generate a 3D information map at an urban scale.

Deep learning-based three-dimensional crack damage detection method using point clouds without color information

Automated high-precision crack detection on building structures under poor lighting conditions poses a significant challenge for traditional image-based methods. Overcoming this challenge is crucial to enhance the practical applicability of structural health monitoring and rapid damage assessment, especially in post-disaster scenarios like earthquakes. To address this challenge, this paper presents a deep learning-based three-dimensional crack detection method that utilizes light detection and ranging (LiDAR) point cloud data. Our method is specifically designed to address crack detection without relying on color information input, resulting in high-precision and robust apparent damage detection. The key contribution of this paper is the NL-3DCrack model, which enables automated three-dimensional crack semantic segmentation. This model comprises a feature embedding module, an incomplete neighbor feature extraction module, a decoder, and morphological filtering. Notably, we introduce an innovative incomplete neighbor mechanism to effectively mitigate the impact of outliers. To validate the effectiveness of our proposed method, we establish two three-dimensional crack detection datasets, namely the Luding dataset and the terrestrial laser scanner dataset, which are based on earthquake disasters. Experimental results demonstrate that our method achieves remarkable performance, with an intersection-over-union of 39.62% and 51.33% on the respective test sets, surpassing existing point cloud-based semantic segmentation models. Ablation experiments further confirm the effectiveness of our approach. In summary, our method showcases exceptional crack detection performance on LiDAR data using only XYZI channels. With its high precision and reliable results, it offers significant utility in real-world applications, contributing to improved structural health monitoring and rapid damage assessment after disasters, particularly in post-earthquake scenarios.

Wind Profile Reconstruction Based on Convolutional Neural Network for Incoherent Doppler Wind LiDAR

The rapid development of artificial intelligence (AI) and deep learning has revolutionized the field of data analysis in recent years, including signal data acquired by remote sensors. Light Detection and Ranging (LiDAR) technology is widely used in atmospheric research for measuring various atmospheric parameters. Wind measurement using LiDAR data has traditionally relied on the spectral centroid (SC) algorithm. However, this approach has limitations in handling LiDAR data, particularly in low signal-to-noise ratio (SNR) regions. To overcome these limitations, this study leverages the capabilities of customized deep-learning techniques to achieve accurate wind profile reconstruction. The study uses datasets obtained from the European Centre for Medium Weather Forecasting (ECMWF) Reanalysis v5 (ERA5) and the mobile Incoherent Doppler LiDAR (ICDL) system constructed by the University of Science and Technology of China. We present a simulation-based approach for generating wind profiles from the statistical data and the associated theoretical calculations. Whereafter, our team constructed a convolutional neural network (CNN) model based on the U-Net architecture to replace the SC algorithm for LiDAR data post-processing. The CNN-generated results are evaluated and compared with the SC results and the ERA5 data. This study highlights the potential of deep learning-based techniques in atmospheric research and their ability to provide more accurate and reliable results.

Wildfire response of forest species from multispectral LiDAR data. A deep learning approach with synthetic data

Forests play a crucial role as the lungs and life-support system of our planet, harbouring 80% of the Earth's biodiversity. However, we are witnessing an average loss of 480 ha of forest every hour because of destructive wildfires spreading across the globe. To effectively mitigate the threat of wildfires, it is crucial to devise precise and dependable approaches for forecasting fire dynamics and formulating efficient fire management strategies, such as the utilisation of fuel models.The objective of this study was to enhance forest fuel classification that considers only structural information, such as the Prometheus model, by integrating data on the fire responses of various tree species and other vegetation elements, such as ground litter and shrubs. This distinction can be achieved using multispectral (MS) Light Detection and Ranging (LiDAR) data in mixed forests. The methodology involves a novel approach in semantic classifications of forests by generating synthetic data with semantic labels regarding fire responses and reflectance information at different spectral bands, as a real MS scanner device would detect. Forests, which are highly intricate environments, present challenges in accurately classifying point clouds. To address this complexity, a deep learning (DL) model for semantic classification was trained on synthetic point clouds in different formats to achieve the best performance when leveraging MS data.Forest plots in the study region were scanned using different Terrestrial Laser Scanning sensors at wavelengths of 905 and 1550 nm. Subsequently, an interpolation process was applied to generate the MS point clouds of each plot, and the trained DL model was applied to classify them. These classifications surpassed the average thresholds of 90% and 75% for accuracy and intersection over union, respectively, resulting in a more precise categorisation of fuel models based on the distinct responses of forest elements to fire. The results of this study reveal the potential of MS LiDAR data and DL classification models for improving fuel model retrieval in forest ecosystems and enhancing wildfire management efforts.

System for Detecting Moving Objects Using 3D Li-DAR Technology

The “System for Detecting Moving Objects Using 3D LiDAR Technology” introduces a groundbreaking method for precisely identifying and tracking dynamic entities within a given environment. By harnessing the capabilities of advanced 3D LiDAR (Light Detection and Ranging) technology, the system constructs intricate three-dimensional maps of the surroundings using laser beams. Through continuous analysis of the evolving data, the system adeptly discerns and monitors the movement of objects within its designated area. What sets this system apart is its innovative integration of a multi-sensor approach, combining LiDAR data with inputs from various other sensor modalities. This fusion of data not only enhances accuracy but also significantly boosts the system’s adaptability, ensuring robust performance even in challenging environmental conditions characterized by low visibility or erratic movement patterns. This pioneering approach fundamentally improves the precision and reliability of moving object detection, thereby offering a valuable solution for a diverse array of applications, including autonomous vehicles, surveillance, and robotics.

The Use of Lidar and Artificial Intelligence Algorithms for Detection and Size Estimation of Potholes

Road potholes have a well-known impact on driving quality and safety. Therefore, timely mitigation of potholes is critical for the safety of road users. However, efficient and timely maintenance relies on the presence of an effective process for pothole detection. Currently, transportation agencies primarily rely on manual inspection and road user reporting. These methods are subjective, prone to inaccuracy, and some are also laborious and time-consuming. An ideal pothole detection system would be accurate, objective, automated, and relatively inexpensive. In this context, accuracy encompasses three distinct performance areas: detection, localization, and size estimation. This study explores the potential of utilizing a mobile light detection and ranging (LiDAR) for accurate detection and size estimation, along with a global navigation satellite system (GNSS) receiver for localization, to develop an effective pothole surveillance system. To achieve this objective, the study proposes a four-step framework. Firstly, the LiDAR data are processed to generate ring-wise cross-sectional images. Secondly, a deep learning object detection network is trained to predict the presence and size of potholes. Thirdly, the ring-wise inferences are aggregated to produce a final decision. Lastly, the aggregated inferences are synchronized with GNSS locations to generate inspection maps. The system’s performance was validated using multiple road strips, never seen by the model, containing potholes of different sizes and shapes. The results demonstrated the effectiveness and accuracy of the proposed system. Overall, this research contributes to the research on LiDAR-based pothole inspection by proposing a novel four-step framework and incorporating it into an end-to-end pothole detection system, which can greatly improve the efficiency of pothole maintenance and enhance the safety of road users.

Driving Factors and Spatial Distribution of Aboveground Biomass in the Managed Forest in the Terai Region of Nepal

Above-ground biomass (AGB) is affected by numerous factors, including topography, climate, land use, or tree/forest attributes. Investigating the distribution and driving factors of AGB within the managed forests in Nepal is crucial for developing effective strategies for climate change mitigation, and sustainable forest management and conservation. A total of 110 field plots (circular 0.02 ha plots with a 9 m radius), and airborne laser scanning (ALS)-light detection and ranging (LiDAR) data were collected in 2021. The random forest (RF) model was employed to predict the AGB at a 30 m × 30 m resolution based on 32 LiDAR metrics derived from ALS returns. The study assessed the relationships between the AGB distribution and nine independent variables using statistical techniques like the random forest model and partial dependence plots. Results showed that the mean value of the estimated AGB was 120 tons/ha, ranging from 0 to 446.42 tons/ha. AGB showed higher values in the northeast and southeast regions, gradually decreasing towards the northwest. Land use land cover, mean annual temperature, and mean annual precipitation were identified as the primary factors influencing the variability in AGB distribution, accounting for 64% of the variability. Elevation, slope, and distance from rivers were positively correlated with AGB, while proximity to roads had a negative correlation. The increase in precipitation and temperature contributed to the initial rise in AGB, but beyond a certain lag, these variables led to a decline in AGB. This study showed the efficiency of the random forest model and partial dependence plots in examining the relationship between the AGB and its driving factors within managed forests. The study highlights the importance of understanding the AGB driving factors and utilizing LiDAR data for informed decisions regarding the region’s sustainable forest management and climate change mitigation efforts.

Investigation of the use of topographic data derived from Pléiades imagery for high-resolution hillslope-scale morphometry

Obtaining accurate representations of the Earth surface topography is paramount in many geomorphological investigations. While regional scale studies use Digital Elevation Models (DEM) with resolution of the order of 10–100 m, in many applications resolution <5 m are mandatory to appropriately represent landforms and processes of interest. In particular, hillslopes, which cover the most of continental surfaces, have typical length of the order of 100 m. For that reason, high resolution topographic data are mandatory to correctly represent their morphology. The massive use of higher resolution topographic data has been enabled by a vast array of methodological developments, among which terrestrial and airborne Light Detection And Ranging (LiDAR) or uncrewed aerial vehicle-based photogrammetric approaches are the most commonly used. However, such techniques might be difficult to deploy and not cost-effective, to cover large areas in remote locations.In this study, we present an exploration of the potential of topographic data derived from Pléiades imagery for the characterizing hillslope morphology. We focus on the Gabilan Mesa area in California, where a LiDAR DEM is available and is used as a benchmark. We systematically compare Pléiades-derived topographic data with the DEM constructed from LiDAR data, starting from simple elevation analysis and then using topographic derivatives, such as slope or curvature, which are commonly used in morphometric studies. Finally, we take advantage of the high resolution of the datasets to extract and compare hillslope-scale morphometric parameters, such as length, relief, or hilltop curvature. While some particular situations, such as planar and horizontal surfaces, requires specific attention in terms of the relative differences between the two type of data, the absolute values for the metrics are in good agreement over their relevant range of utilization for hillslope-scale geomorphology.

Quantitative assessment of earthquake-induced building damage at regional scale using LiDAR data

Regional-scale assessment of the damage caused by earthquakes to structures is crucial for post-disaster management. While remote sensing techniques can be of great help for a quick post-event structural assessment of large areas, currently available methods are limited to the detection of severely-damaged buildings. Furthermore, remote sensing-based assessment methods typically provide only qualitative results, as they lack integration with information on the building’s behaviour in response to seismic-induced ground shaking. In this study, we developed a new methodology that uses airborne Light Detection And Ranging (LiDAR) data in combination with structural indicators of building response to provide a quantitative assessment of earthquake-induced damage at a regional scale. LiDAR datasets collected before and after an earthquake are used to measure residual displacements of building roofs. The resulting lateral drift estimations are used to quantify the level of damage for a specific building typology. Application to the LiDAR datasets collected before and after the 2014 earthquake in Napa Valley, California, demonstrates the capability of the proposed method to detect moderate levels of structural damage, proving its potential for faster and more accurate support to post-disaster management.

Fine-Scale Quantification of Absorbed Photosynthetically Active Radiation (APAR) in Plantation Forests with 3D Radiative Transfer Modeling and LiDAR Data.

Quantifying the relationship between light and stands or individual trees is of great significance in understanding tree competition, improving forest productivity, and comprehending ecological processes. However, accurately depicting the spatiotemporal variability of light under complex forest structural conditions poses a challenge, especially for precise forest management decisions that require a quantitative study of the relationship between fine-scale individual tree structure and light. 3D RTMs (3-dimensional radiative transfer models), which accurately characterize the interaction between solar radiation and detailed forest scenes, provide a reliable means for depicting such relationships. This study employs a 3D RTM and LiDAR (light detection and ranging) data to characterize the light environment of larch plantations at a fine spatiotemporal scale, further investigating the relationship between absorbed photosynthetically active radiation (APAR) and forest structures. The impact of specific tree structural parameters, such as crown diameter, crown area, crown length, crown ratio, crown volume, tree height, leaf area index, and a distance parameter assessing tree competition, on the daily-scale cumulative APAR per tree was investigated using a partial least squares regression (PLSR) model. Furthermore, variable importance in projection (VIP) was also calculated from the PLSR. The results indicate that among the individual tree structure parameters, crown volume is the most important one in explaining individual tree APAR (VIP = 4.19), while the competition from surrounding trees still plays a role in explaining individual tree APAR to some extent (VIP = 0.15), and crown ratio contributes the least (VIP = 0.03). Regarding the spatial distribution of trees, the average cumulative APAR per tree of larch plots does not increase with an increase in canopy gap fraction. Tree density and average cumulative APAR per tree were fitted using a natural exponential equation, with a coefficient of determination (R2 = 0.89), and a small mean absolute percentage error (MAPE = 0.03). This study demonstrates the potential of combining 3D RTM with LiDAR data to quantify fine-scale APAR in plantations, providing insights for optimizing forest structure, enhancing forest quality, and implementing precise forest management practices, such as selective breeding for superior tree species.

Dams overtopping scenarios from catastrophic landslides in mountains’ headwaters Case study in Kobe City, Japan

As a Mw 8.0 Nankai Trough Earthquake is predicted with an 80% probability of occurrence within the next 30 years, the efficiency of check dams in the mountains above Kobe City is a crucial question when considering co-seismic landslide disaster risk management. In the present contribution the author aimed to define which subsection of the Sumiyoshigawa watershed may be more prone to generate impacts in downstream Kobe City from catastrophic landslides in the headwaters. For this purpose, the present state of the check dams network was analysed from the 2018 LiDAR (Light Detection and Ranging) data, considering the progressive infilling of the structures. As a result, the capacity of the dams in 2018 dropped by about 1/10 of the original designed capacity, arguably because of the heavy rainfall events in 2014 and 2018. Despite this evolution, however, landslides &gt; 20,000 m3 starting from only one tributary can fill all the dams and flow downstream. From this data, it is possible to prioritize dam curation programs, especially because population ageing and shrinking is reducing the manpower and the funds available to maintain the check dams.

MDU‐sampling: Multi‐domain uniform sampling method for large‐scale outdoor LiDAR point cloud registration

AbstractSampling is a crucial concern for outdoor light detection and ranging (LiDAR) point cloud registration due to the large amounts of point cloud. Numerous algorithms have been devised to tackle this issue by selecting key points. However, these approaches often necessitate extensive computations, giving rise to challenges related to computational time and complexity. This letter proposes a multi‐domain uniform sampling method (MDU‐sampling) for large‐scale outdoor LiDAR point cloud registration. The feature extraction based on deep learning aggregates information from the neighbourhood, so there is redundancy between adjacent features. The sampling method in this paper is carried out in the spatial and feature domains. First, uniform sampling is executed in the spatial domain, maintaining local point cloud uniformity. This is believed to preserve more potential point correspondences and is beneficial for subsequent neighbourhood information aggregation and feature sampling. Subsequently, a secondary sampling in the feature domain is performed to reduce redundancy among the features of neighbouring points. Notably, only points on the same ring in LiDAR data are considered as neighbouring points, eliminating the need for additional neighbouring point search and thereby speeding up processing rates. Experimental results demonstrate that the approach enhances accuracy and robustness compared with benchmarks.

Importance of high-resolution spatial data for the detection of winter wildlife responses to edges

Several wildlife species are thought to avoid edges of large habitat gaps, such as clear-cuts, but detailed evidence is rarely available for edges of smaller gaps. We compared the responses of nine wintering mammal species to forest edges in southern Quebec, Canada, using high-resolution spatial data from Light Detection And Ranging (LiDAR) and low-resolution photo-interpretation. We defined edges of open areas as roads, lakes, rivers, or forest open areas. We geolocated mammal snow tracks along systematic transect lines between 2009 and 2018. We compared distances of snow tracks and reference points along transects to the nearest edge with linear models. LiDAR data revealed five species avoiding forest open area edges, whereas no avoidance was shown using photo-interpretation data. Weasels (Mustela sp.) were the only species showing a positive association with forest open area edges using photo-interpreted data. No significant response was detected for river or lake edges. Four species were positively associated with road edges. We conclude that avoidance of small forest open area edges is widespread in our study area, but it can only be detected with high-resolution spatial data. Our results imply that edge effect can operate at a fine scale and using appropriate spatial resolution is crucial to detect such effects.

UAS Quality Control and Crop Three-Dimensional Characterization Framework Using Multi-Temporal LiDAR Data

Information on a crop’s three-dimensional (3D) structure is important for plant phenotyping and precision agriculture (PA). Currently, light detection and ranging (LiDAR) has been proven to be the most effective tool for crop 3D characterization in constrained, e.g., indoor environments, using terrestrial laser scanners (TLSs). In recent years, affordable laser scanners onboard unmanned aerial systems (UASs) have been available for commercial applications. UAS laser scanners (ULSs) have recently been introduced, and their operational procedures are not well investigated particularly in an agricultural context for multi-temporal point clouds. To acquire seamless quality point clouds, ULS operational parameter assessment, e.g., flight altitude, pulse repetition rate (PRR), and the number of return laser echoes, becomes a non-trivial concern. This article therefore aims to investigate DJI Zenmuse L1 operational practices in an agricultural context using traditional point density, and multi-temporal canopy height modeling (CHM) techniques, in comparison with more advanced simulated full waveform (WF) analysis. Several pre-designed ULS flights were conducted over an experimental research site in Fargo, North Dakota, USA, on three dates. The flight altitudes varied from 50 m to 60 m above ground level (AGL) along with scanning modes, e.g., repetitive/non-repetitive, frequency modes 160/250 kHz, return echo modes (1n), (2n), and (3n), were assessed over diverse crop environments, e.g., dry corn, green corn, sunflower, soybean, and sugar beet, near to harvest yet with changing phenological stages. Our results showed that the return echo mode (2n) captures the canopy height better than the (1n) and (3n) modes, whereas (1n) provides the highest canopy penetration at 250 kHz compared with 160 kHz. Overall, the multi-temporal CHM heights were well correlated with the in situ height measurements with an R2 (0.99–1.00) and root mean square error (RMSE) of (0.04–0.09) m. Among all the crops, the multi-temporal CHM of the soybeans showed the lowest height correlation with the R2 (0.59–0.75) and RMSE (0.05–0.07) m. We showed that the weaker height correlation for the soybeans occurred due to the selective height underestimation of short crops influenced by crop phonologies. The results explained that the return echo mode, PRR, flight altitude, and multi-temporal CHM analysis were unable to completely decipher the ULS operational practices and phenological impact on acquired point clouds. For the first time in an agricultural context, we investigated and showed that crop phenology has a meaningful impact on acquired multi-temporal ULS point clouds compared with ULS operational practices revealed by WF analyses. Nonetheless, the present study established a state-of-the-art benchmark framework for ULS operational parameter optimization and 3D crop characterization using ULS multi-temporal simulated WF datasets.

Estimation of Above-Ground Forest Biomass in Nepal by the Use of Airborne LiDAR, and Forest Inventory Data

Forests play a significant role in sequestering carbon and regulating the global carbon and energy cycles. Accurately estimating forest biomass is crucial for understanding carbon stock and sequestration, forest degradation, and climate change mitigation. This study was conducted to estimate above-ground biomass (AGB) and compare the accuracy of the AGB estimating models using LiDAR (light detection and ranging) data and forest inventory data in the central Terai region of Nepal. Airborne LiDAR data were collected in 2021 and made available by Nepal Ban Nigam Limited, Government of Nepal. Thirty-two metrics derived from the laser-scanned LiDAR point cloud data were used as predictor variables (independent variables), while the AGB calculated from field data at the plot level served as the response variable (dependent variable). The predictor variables in this study were LiDAR-based height and canopy metrics. Two statistical methods, the stepwise linear regression (LR) and the random forest (RF) models, were used to estimate forest AGB. The output was an accurate map of AGB for each model. The RF method demonstrated better precision compared to the stepwise LR model, as the R2 metric increased from 0.65 to 0.85, while the RMSE values decreased correspondingly from 105.88 to 60.9 ton/ha. The estimated AGB density varies from 0 to 446 ton/ha among the sample plots. This study revealed that the height-based LiDAR metrics, such as height percentile or maximum height, can accurately and precisely predict AGB quantities in tropical forests. Consequently, we confidently assert that substantial potential exists to monitor AGB levels in forests effectively by employing airborne LiDAR technology in combination with field inventory data.

A new method for voxel‐based modelling of three‐dimensional forest scenes with integration of terrestrial and airborne LiDAR data

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

Mapping tree species diversity in a typical natural secondary forest by combining multispectral and LiDAR data

Accurate monitoring of tree species diversity is crucial for understanding the dynamic changes in tree species diversity and its relationships with other services and functions in forest ecosystems. Traditional optical remote sensing data have been widely used for monitoring tree species diversity based on the spectral variation hypothesis (SVH). However, this method cannot capture the three-dimensional structural variations in complex species compositions under different stand conditions. In this study, we modeled tree species diversity in terms of spectral variation and stand structural complexity in a typical natural secondary forest in Northeast China by combining Sentinel-2 data and UAV-borne light detection and ranging (LiDAR) point cloud data. First, species diversity indices (including the Shannon index H' and Simpson index D1) were derived from 60 field-measured plots. Second, recursive feature elimination (RFE) was utilized for feature filtering of ten spectral bands and four vegetation indices extracted from Sentinel-2 data and Rao's Q index, as well as eleven features extracted from LiDAR point clouds reflecting the complexity of the stand structure. Subsequently, the random forest method was utilized to fit and predict the relationship between the remote sensing feature set and tree species diversity. The results showed that the use of multisource remote sensing feature set to estimate tree species diversity had the highest accuracy (R2 = 0.44, RMSE = 0.28 for H') compared to the use of only one data source. Moreover, when using a single remote sensing feature set, the estimation accuracy of the optical remote sensing feature set is higher than that of the LiDAR feature set for H' and D1, and the NIRv is the most influential spectral feature. This study clarified the value of spectral variation and productivity heterogeneity embodied in optical remote sensing features for monitoring tree species diversity, as well as evaluating the shortcomings and possibilities of using LiDAR point cloud features independently, and fully confirmed the positive significance of complementary effects between multisource remote sensing feature sets.

A Joint Convolutional Cross ViT Network for Hyperspectral and Light Detection and Ranging Fusion Classification

The fusion of hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data for classification has received widespread attention and has led to significant progress in research and remote sensing applications. However, existing common CNN architectures suffer from the significant drawback of not being able to model remote sensing images globally, while transformer architectures are not able to capture local features effectively. To address these bottlenecks, this paper proposes a classification framework for multisource remote sensing image fusion. First, a spatial and spectral feature projection network is constructed based on parallel feature extraction by combining HSI and LiDAR data, which is conducive to extracting joint spatial, spectral, and elevation features from different source data. Furthermore, in order to construct local–global nonlinear feature mapping more flexibly, a network architecture coupling together multiscale convolution and a multiscale vision transformer is proposed. Moreover, a plug-and-play nonlocal feature token aggregation module is designed to adaptively adjust the domain offsets between different features, while a class token is employed to reduce the complexity of high-dimensional feature fusion. On three open-source remote sensing datasets, the performance of the proposed multisource fusion classification framework improves about 1% to 3% over other state-of-the-art algorithms.