Scanning Lidar Research Articles

Liquefaction-induced ground and building interactions in İskenderun from the 2023 Kahramanmaraş earthquake sequence

Significant and widespread liquefaction occurred in İskenderun during the 2023 moment magnitude (Mw) 7.8 Kahramanmaraş earthquake. Liquefaction effects on buildings were observed in several areas of İskenderun, predominantly in areas of reclaimed land and near historic shorelines. Liquefaction-induced building settlements were particularly concentrated in the Çay District, which is almost entirely reclaimed land. Liquefaction-induced ground and building settlements were either marginal or not apparent in areas away from the historical shorelines. Building settlement and ground deformation were documented at 26 buildings in İskenderun through lidar scans and laser-level hand measurements. Liquefaction-induced building settlements ranged from 0 to 740 mm. Building-ground interactions were evident from hogging ground deformations, including cases where buildings deformed nearby ground and damaged nearby buildings, and sagging buildings. Historic land development affected the spatial extent of observed liquefaction-induced building damage. Representative liquefaction-induced building settlement and building interaction case histories are discussed and key insights are shared.

3D point cloud-based place recognition: a survey

Place recognition is a fundamental topic in computer vision and robotics. It plays a crucial role in simultaneous localization and mapping (SLAM) systems to retrieve scenes from maps and identify previously visited places to correct cumulative errors. Place recognition has long been performed with images, and multiple survey papers exist that analyze image-based methods. Recently, 3D point cloud-based place recognition (3D-PCPR) has become popular due to the widespread use of LiDAR scanners in autonomous driving research. However, there is a lack of survey paper that discusses 3D-PCPR methods. To bridge the gap, we present a comprehensive survey of recent progress in 3D-PCPR. Our survey covers over 180 related works, discussing their strengths and weaknesses, and identifying open problems within this domain. We categorize mainstream approaches into feature-based, projection-based, segment-based, and multimodal-based methods and present an overview of typical datasets, evaluation metrics, performance comparisons, and applications in this field. Finally, we highlight some promising research directions for future exploration in this domain.

Accurate semi‐direct lidar‐inertial odometry based on distance and normal direction

AbstractTo improve the autonomous navigation capability of unmanned platforms, an accurate semi‐direct lidar‐inertial odometry algorithm with a new point cloud alignment evaluation metric is proposed. The lidar scan points are corrected through IMU (Inertial Measurement Unit) backward propagation, and the navigation states are predicted through IMU forward propagation. In the update of the navigation states, the point cloud alignment is evaluated based on the distances and the minimum normal direction deviations between the lidar scan points and the local fitted planes of the built map, achieving accurate observations for the navigation states. The proposed algorithm updates navigation states based on the iterative extended Kalman filter. Experiments indicate that the proposed algorithm has higher positioning accuracy than the state‐of‐the‐art method, which gets smaller positioning error (7.868 m) and 52.3% improvement on average.

Factors controlling volcanic debris flows two decades after the 1990–1995 eruption at Mount Unzen in Japan

Rainfall-triggered volcanic debris flows (VDFs), or lahars, frequently occur during and/or immediately after eruptions, often because of decreases in the infiltration capacity resulting from the deposition of fine volcanic materials. Although the infiltration capacity recovers and the frequency of VDFs decreases within a decade, VDFs have continued to occur over several decades after the eruption of some volcanoes. No study has yet identified the factors controlling sediment discharge of VDFs for a specific volcano after the recovery of infiltration capacity to the pre-eruptive level. We analyzed topographic changes and sediment volumes between 2003 and 2020, using 16 digital elevation models (DEMs) and orthophotographs from aerial lidar scanning for the Tansandani and Gokurakudani gullies of Mount Unzen where the infiltration capacity has recovered after the most recent eruption in 1990–1995. We also analyzed the frequency and magnitude of VDFs in 2003–2020. Topographic changes differed before and after three large-scale VDFs in 2015 and 2016. The total volume of the three VDFs was 140 × 103 m3. Between 2003 and 2014, the width and depth of the gullies continuously increased, with the changes in the intervals of successive DEMs averaging below 5 m. Between 2017 and 2020, gully cross-sections remained U-shaped and changed little, while the channel gradient gradually decreased. U-shaped gullies promote the dispersion of VDFs and their gentle slopes reduce the velocity of VDFs. As a result, the frequency and magnitude of VDFs decreased, and deposition and erosion in the gullies were almost balanced between 2017 and 2020. Thus, we conclude that topography is the main factor that controls the occurrence and magnitude of debris flows and therefore sediment discharge after the recovery of infiltration capacity to the pre-eruptive level.

Reviving Antiquity in the Digital Era: Digitization, Semantic Curation, and VR Exhibition of Contemporary Dresses

In this paper, we present a comprehensive methodology to support the multifaceted process involved in the digitization, curation, and virtual exhibition of cultural heritage artifacts. The proposed methodology is applied in the context of a unique collection of contemporary dresses inspired by antiquity. Leveraging advanced 3D technologies, including lidar scanning and photogrammetry, we meticulously captured and transformed physical garments into highly detailed digital models. The postprocessing phase refined these models, ensuring an accurate representation of the intricate details and nuances inherent in each dress. Our collaborative efforts extended to the dissemination of this digital cultural heritage, as we partnered with the national aggregator in Greece, SearchCulture, to facilitate widespread access. The aggregation process streamlined the integration of our digitized content into a centralized repository, fostering cultural preservation and accessibility. Furthermore, we harnessed the power of these 3D models to transcend traditional exhibition boundaries, crafting a virtual experience that transcends geographical constraints. This virtual exhibition not only enables online exploration but also invites participants to immerse themselves in a captivating virtual reality environment. The synthesis of cutting-edge digitization techniques, cultural aggregation, and immersive exhibition design not only contributes to the preservation of contemporary cultural artifacts but also redefines the ways in which audiences engage with and experience cultural heritage in the digital age.

Canopy Structural Changes in Black Pine Trees Affected by Pine Processionary Moth Using Drone-Derived Data

Pine species are a key social and economic component in Mediterranean ecosystems, where insect defoliations can have far-reaching consequences. This study aims to quantify the impact of pine processionary moth (PPM) on canopy structures, examining its evolution over time at the individual tree level using high-density drone LiDAR-derived point clouds. Focusing on 33 individuals of black pine (Pinus nigra)—a species highly susceptible to PPM defoliation in the Mediterranean environment—bitemporal LiDAR scans were conducted to capture the onset and end of the major PPM feeding period in winter. Canopy crown delineation performed manually was compared with LiDAR-based methods. Canopy metrics from point clouds were computed for trees exhibiting contrasting levels of defoliation. The structural differences between non-defoliated and defoliated trees were assessed by employing parametric statistical comparisons, including analysis of variance along with post hoc tests. Our analysis aimed to distinguish structural changes resulting from PPM defoliation during the winter feeding period. Outcomes revealed substantive alterations in canopy cover, with an average reduction of 22.92% in the leaf area index for defoliated trees, accompanied by a significant increase in the number of returns in lower tree crown branches. Evident variations in canopy density were observed throughout the feeding period, enabling the identification of two to three change classes using LiDAR-derived canopy density metrics. Manual and LiDAR-based crown delineations exhibited minimal differences in computed canopy LiDAR metrics, showcasing the potential of LiDAR delineations for broader applications. PPM infestations induced noteworthy modifications in canopy morphology, affecting key structural parameters. Drone LiDAR data emerged as a comprehensive tool for quantifying these transformations. This study underscores the significance of remote sensing approaches in monitoring insect disturbances and their impacts on forest ecosystems.

Development of a near-infrared single-photon 3D imaging LiDAR based on 64×64 InGaAs/InP array detector and Risley-prism scanner.

A near-infrared single-photon lidar system, equipped with a 64×64 resolution array and a Risley prism scanner, has been engineered for daytime long-range and high-resolution 3D imaging. The system's detector, leveraging Geiger-mode InGaAs/InP avalanche photodiode technology, attains a single-photon detection efficiency of over 15% at the lidar's 1064 nm wavelength. This efficiency, in tandem with a narrow pulsed laser that boasts a single-pulse energy of 0.5 mJ, facilitates 3D imaging capabilities for distances reaching approximately 6 kilometers. The Risley scanner, composing two counter-rotating wedge prisms, is designed to perform scanning measurements across a 6-degree circular field-of-view. Precision calibration of the scanning angle and the beam's absolute direction was achieved using a precision dual-axis turntable and a collimator, culminating in 3D imaging with an exceptional scanning resolution of 28 arcseconds. Additionally, this work has developed a novel spatial domain local statistical filtering framework, specifically designed to separate daytime background noise photons from the signal photons, enhancing the system's imaging efficacy in varied lighting conditions. This paper showcases the advantages of array-based single-photon lidar image-side scanning technology in simultaneously achieving high resolution, a wide field-of-view, and extended detection range.

A LIVING LAB APPROACH FOR CONCURRENT 3D DOCUMENTATION OF AN ARCHITECTURAL RENOVATION PROJECT – A SHOWCASE OF GUNSAN CIVIC CULTURAL CENTER

Abstract. This paper explores the integration of 3D documentation within a living lab framework for the renovation of the Gunsan Civic Cultural Center. The study emphasizes the importance of architectural renovation in contemporary sustainability and social resilience, highlighting the unique challenges of adapting existing structures. Our approach combines advanced 3D scanning techniques, such as drone filming, photogrammetry, LiDAR scanning, and hybrid modeling, with the living lab methodology, enabling real-world experimentation and stakeholder engagement. Our showcase on the Gunsan Civic Cultural Center illustrates the practical application of these techniques in material reuse and cultural performance planning, emphasizing community participation and collaborative decision-making. The living lab approach, central to our methodology, fosters dynamic interaction among architects, builders, community members, and local authorities, enhancing communication and project outcomes. This study contributes to the field by showcasing how concurrent 3D documentation in a living lab setting can revitalize heritage sites, ensuring their sustainable future while actively involving the community. The lessons and discussions drawn from this project highlight the potential of this approach in broader architectural and urban development contexts.

CONNECTING EMBEDDED HISTORY OF THE URBAN INFRASTRUCTURE TO THE CONTEMPORARY CITY: A CASE STUDY OF GWANGHWAMUN WOLDAE RESTORATION

Abstract. The captured 3D digital record of the excavation site represents a momentary state of the city, but also conveys rich evidence and stories as an urban environment created by the interaction of actors, artifacts, and institutions surrounding the place. These digital records combine multiple temporal layers of complex urban scenes and require interpretation by multiple stakeholders for comprehensive decision-making. This study revealed that in the context of Seoul, the Cultural Heritage Administration and the City of Seoul have made significant use of digital records in cultural heritage restoration from the perspective of preservation and utilization. The two organizations collaborated on the Gwanghwamun Woldae excavation survey recently. The site has been dealing with the multi-layered time and space of complex urban sites for over 40 years while carrying out urban planning and cultural heritage preservation. Through a case study of the Gwanghwamun Woldae excavation site, we articulate the nature of governance in digital documentation to extend its application. The goal is to highlight the urban cross-section and historical layers of urban infrastructure captured digitally - LiDAR and photogrammetric scanning - at the Gwanghwamun Woldae excavation site. This involves describing the chronological layers of tram lines, streets and buildings from different eras and linking them to the current urban context of Seoul through analog historical records such as maps and plan diagrams. Ultimately, we discuss the value of digital restoration of technologies, events and landscapes as a record of the process valuing modern urban heritage.

Analysis of Beach Cusp Formation and Evolution Using High‐Frequency 3D Lidar Scans

AbstractIn this study, beach cusp characteristics were explored using 15 months of 3D lidar scans collected hourly at the Field Research Facility in Duck, NC. Fourier analyses were performed on lidar‐derived beach elevation contours to generate spatial cusp spectra. Active cusp events were identified on the basis of the location and magnitude of each spectrum's peak and used to evaluate conditions during cusp formation and evolution. Cusps primarily developed during times with normally‐incident, long period, low energy wave conditions with low frequency spread, and reflective beach conditions. The upper and lower beaches often exhibited different behavior and morphologies, with persistent upper‐beach cusps lasting days to months and dynamic lower‐beach cusps evolving over individual tidal cycles. At times, beaches exhibiting multiple cusp systems reverted to a single cusp system extending over the entire beach when the high‐tide waterline reached the upper‐beach cusps, with the location and spacing of the resulting lower‐beach cusps controlled by the upper‐beach cusps. These observations are consistent with a “morphological coupling” hypothesis proposing that hydrodynamic‐morphodynamic feedbacks between the swash and upper‐beach cusps can result in the formation of lower‐beach cusps with a related wavelength as the tide falls. However, there were also times when the high‐tide waterline reached the upper‐beach cusps that did not result in a unified beach state. These results suggest that while morphological coupling is often an important factor in controlling the development of new lower‐beach cusps, this coupling cannot initiate cusp formation in hydrodynamic conditions outside those favorable for cusp activity.

Transformer based 3D semantic segmentation of urban bicycle infrastructure

ABSTRACT This work investigates Point Cloud Semantic Segmentation (PCSS) to detect bicycle paths from 3D point cloud data of bicycle-mounted LiDARs. For the task of PCSS, an existing Convolutional Neural Network architecture (CNN) is first pre-trained on the Semantic KITTI data set, an important data set for PCSS in LiDAR scans of driving situations. Furthermore, a new, semantically-labelled 3D LiDAR data set, the Salzburg Bicycle LiDAR Data Set (SBLD), is presented, which consists of 16,008 point clouds recorded by a ROS2-enabled sensor bicycle with five 3D LiDARs covering every direction. After fine-tuning the CNN on the SBLD train set, the segmentation performance is evaluated on the SBLD test set. The CNN shows promising results in recognising bike paths, vegetation, terrain and buildings in the SBLD. To further push the segmentation performance, the existing CNN is enhanced with self-attention blocks. The evaluation of this enhanced architecture shows a performance gain of 2.79% points compared to the original architecture. The proposed segmentation approach shows practical potential for detecting bike paths. The SBLD is provided to the scientific community to further drive research in the field of sensor bicycles.

Handheld lidar as a tool for characterizing wood‐rich river corridors

AbstractWood accumulations influence geomorphic, hydraulic, and ecologic functions within a river corridor, but characterizing these accumulations presents challenges across a range of field and remote sensing methodologies. We evaluate the ability of handheld lidar scanners, specifically lidar‐scanning capabilities of a fourth‐generation iPad Pro, to collect three‐dimensional wood accumulation data, which can be used to inform measurements of wood volume, porosity, complexity, and roughness. We discuss the potential and limitations of this novel methodology for river research and management. We found that handheld lidar presents a cost‐effective input for data‐processing workflows that field measurements of wood accumulation dimensions cannot as easily replicate including (1) a user‐friendly means of data collection and visualization; (2) accurate comparisons of wood volume over time; (3) integration into workflows to measure porosity parameters; and (4) potential use in informing hydraulic and morphodynamic models. Consideration of study area constraints and intended use of scans are prerequisites to using handheld lidar as an effective tool. We identified some specific limitations of using handheld lidar scanners in wood‐rich river corridors, including (1) scanners perform poorly when wood is under water or surrounded by dense vegetation; (2) scanners require physical access to areas of interest at distances less than 5 m; (3) scans need to be manually georeferenced; and (4) scans require manual measurements for any dimensional data, which still have associated user time and error. Handheld lidar as a scientific tool is rapidly developing and there is substantial room for expansion of applications, utilization, and advances in the use of this tool in river research and management.

FMCW Radar on LiDAR map localization in structural urban environments

AbstractMultisensor fusion‐based localization technology has achieved high accuracy in autonomous systems. How to improve the robustness is the main challenge at present. The most commonly used LiDAR and camera are weather‐sensitive, while the frequency‐modulated continuous wave Radar has strong adaptability but suffers from noise and ghost effects. In this paper, we propose a heterogeneous localization method called Radar on LiDAR Map, which aims to enhance localization accuracy without relying on loop closures by mitigating the accumulated error in Radar odometry in real time. To accomplish this, we utilize LiDAR scans and ground truth paths as Teach paths and Radar scans as the trajectories to be estimated, referred to as Repeat paths. By establishing a correlation between the Radar and LiDAR scan data, we can enhance the accuracy of Radar odometry estimation. Our approach involves embedding the data from both Radar and LiDAR sensors into a density map. We calculate the spatial vector similarity with an offset to determine the corresponding place index within the candidate map and estimate the rotation and translation. To refine the alignment, we utilize the Iterative Closest Point algorithm to achieve optimal matching on the LiDAR submap. The estimated bias is subsequently incorporated into the Radar SLAM for optimizing the position map. We conducted extensive experiments on the Mulran Radar Data set, Oxford Radar RobotCar Dataset, and our data set to demonstrate the feasibility and effectiveness of our proposed approach. Our proposed scan projection descriptors achieves homogeneous and heterogeneous place recognition and works much better than existing methods. Its application to the Radar SLAM system also substantially improves the positioning accuracy. All sequences' root mean square error is 2.53 m for positioning and 1.83° for angle.

Tree Diameter at Breast Height (DBH) Estimation Using an iPad Pro LiDAR Scanner: A Case Study in Boreal Forests, Ontario, Canada

The aim of this study was to determine whether the iPad Pro 12th generation LiDAR sensor is useful to measure tree diameter at breast height (DBH) in natural boreal forests. This is a follow-up to a previous study that was conducted in a research forest and identified the optimal method for (DBH) estimation as a circular scanning and fitting ellipses to 4 cm stem cross-sections at breast height. The iPad Pro LiDAR scanner was used to acquire point clouds for 15 sites representing a range of natural boreal forest conditions in Ontario, Canada, and estimate DBH. The secondary objective was to determine if tested stand (species composition, age, density, understory) or tree (species, DBH) factors affected the accuracy of estimated DBH. Overall, estimated DBH values were within 1 cm of actual DBH values for 78 of 133 measured trees (59%). An RMSE of 1.5 cm (8.6%) was achieved. Stand age had a large effect (>0.15) on the accuracy of estimated DBH values, while density, understory, and DBH had moderate effects (0.05–0.14). No trend was identified between accuracy and stand age. Accuracy improved as understory density decreased and as tree DBH increased. Inertial measurement unit (IMU) and positional accuracy errors with the iPad Pro scanner limit the feasibility of using this device for forest inventories.

Between water and land: Connecting and comparing underwater, terrestrial and airborne remote-sensing techniques

Ostrów Lednicki is one of the most important early medieval sites in Poland. Land and underwater archaeological excavations have been carried out there for many decades. This has allowed for a significant reconnaisance of the Lednica settlement complex. However, further research, especially that carried out underwater and in the shore-zone, has required the use of modern methods involving a multibeam echosounder (MBES), sub-bottom profiler (SBP), caesium magnetometer and LiDAR scans. The primary platform used for combining the underwater and aerial survey data was a Geographic Information System (GIS) environment. This software made it possible to compare and correlate acoustic, magnetic, seismic and laser data. As a result, new important archaeological features were discovered, including two previously unknown bridges and extensive shore-zone fortifications.

Dynamic graph CNN based semantic segmentation of concrete defects and as-inspected modeling

Obtaining accurate information of defective areas of infrastructures helps to perform repair actions more efficiently. Recently, LiDAR scanners have been used for the inspection of surface defects. Moreover, machine learning methods have attracted the attention of researchers for semantic segmentation and classification based on point cloud data. Although much work has been done for processing visual information with images, research on machine learning methods for semantic segmentation of raw point cloud data is still in its early stages. Moreover, LiDAR technology is commonly used to create as-is BIM models. Therefore, the BIM model needs to be integrated with the results of defect semantic segmentation after the LiDAR-based inspection. Addressing the above issues, this paper has the following objectives: (1) Developing a method for point cloud-based concrete surface defects semantic segmentation; and (2) Developing a semi-automated process for as-inspected modeling. The challenges related to the size of the dataset and imbalanced classes are studied. Sensitivity analysis is applied to capture the best combination of hyperparameters and investigate their effects on the network performance. The proposed method resulted in 98.56% and 96.50% recalls for semantic segmentation of cracks and spalls, respectively. Furthermore, post-processing of the results of the concrete surface defects semantic segmentation is done to semi-automate the process of as-inspected modeling. As-inspected BIM includes the updated information of the facilities at the time of data collection. This semi-automated process made it possible to manage and visualize the detected defects by extracting their dimensions and identifying the conditions on the 3D model.

Non-Repetitive Scanning LiDAR Sensor for Robust 3D Point Cloud Registration in Localization and Mapping Applications.

Three-dimensional point cloud registration is a fundamental task for localization and mapping in autonomous navigation applications. Over the years, registration algorithms have evolved; nevertheless, several challenges still remain. Recently, non-repetitive scanning LiDAR sensors have emerged as a promising 3D data acquisition tool. However, the feasibility of this type of sensor to leverage robust point cloud registration still needs to be ascertained. In this paper, we explore the feasibility of one such LiDAR sensor with a Spirograph-type non-repetitive scanning pattern for robust 3D point cloud registration. We first characterize the data of this unique sensor; then, utilizing these results, we propose a new 3D point cloud registration method that exploits the unique scanning pattern of the sensor to register successive 3D scans. The characteristic equations of the unique scanning pattern, determined during the characterization phase, are used to reconstruct a perfect scan at the target distance. The real scan is then compared with this reconstructed scan to extract objects in the scene. The displacement of these extracted objects with respect to the center of the unique scanning pattern is compared in successive scans to determine the transformations that are then used to register these scans. The proposed method is evaluated on two real and different datasets and compared with other state-of-the-art registration methods. After analysis, the performance (localization and mapping results) of the proposed method is further improved by adding constraints like loop closure and employing a Curve Fitting Derivative Filter (CFDT) to better estimate the trajectory. The results clearly demonstrate the suitability of the sensor for such applications. The proposed method is found to be comparable with other methods in terms of accuracy but surpasses them in performance in terms of processing time.

Walking through the abstract[ed] city and co-creating urban space

ABSTRACT This paper explores how co-designing urban walkability can be augmented by an innovative hybrid approach, whereby virtual records and visualisations of the walking experience can enhance the awareness, perceptions and immersion of the participant in both real and virtual spaces. From one side of that model, the research explores how people might be intrigued enough to discover the real context, based on their experience informed and enriched by parallel images of the city. On the other side, the study aimed to develop a critical understanding of urban walking through the lens of 3D high-definition LIDAR scanning technology, where visualisation techniques were used to support studies to explore how the rich experience of walking could be captured and represented. The paper presents a theoretical framework to propose how walking could be promoted, and positively influenced by the urban environment, by regarding the city from the abstract perspective of the virtual point cloud. The research has investigated how and whether a place – real and abstracted – could act as a trigger to produce novel ideas and unfold thoughts in a participatory way. The interlinkages between motion and (visual) perception of the environment as an aesthetic experience were critical to informing how digital technology can be utilised as a virtual space within which the richness of real interactions and experiences with urban space can be represented, refined, interacted with and used within a rich(er) process of co-design.

HPPLO-Net: Unsupervised LiDAR Odometry Using a Hierarchical Point-to-Plane Solver

High-precision LiDAR odometry (LO) plays an essential role in autonomous driving. Generally, due to inaccurate data associations and the existence of outliers, it is a challenging task to estimate ego-motions reliably and efficiently given sparse and complexly distributed point clouds in outdoor environments. In this paper, we propose an unsupervised method for LiDAR odometry, named HPPLO-Net, to predict the relative pose of a LiDAR sensor in a hierarchical way. Specifically, we achieve accurate 6-DoF (Degree of Freedom) pose estimation between the source and target point clouds using a differentiable Point-to-Plane solver with the assistance of scene flow. The novel Point-to-Plane solver consists of a multi-scale aggregation (MSA) normal estimation layer and a differentiable weighted Point-to-Plane SVD module. The MSA layer is introduced to find reliable normal vectors of the pseudo target point cloud by aggregating multi-scale contextual information. The differentiable weighted Point-to-Plane SVD is embedded in the network to solve the pose matrix and alleviate the problem of lacking accurate data association in two LiDAR scans, which exists in the point-to-point alternatives. To reduce the impact of noises and outliers, our method can learn and update the inlier mask and the (residual) flow uncertainty at each layer of the hierarchy. We demonstrate the effectiveness of our method on the KITTI Odometry Dataset, Ford Campus Vision and Lidar DataSet, and the Apollo-SouthBay Dataset. Our method has achieved superior performance than recent unsupervised learning-based methods and some traditional geometry-based methods and has also achieved promising performance close to A-LOAM with mapping optimization. The source code of our method is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/IMRL/HPPLO-Net</uri> .

Investigation of the Spatial and Temporal Variation of Temperature and Water Vapor in Lower Troposphere Using the Combined Backward Raman Scattering LiDAR and Lateral Scanning Raman Scattering LiDAR

Investigation of the Spatial and Temporal Variation of Temperature and Water Vapor in Lower Troposphere Using the Combined Backward Raman Scattering LiDAR and Lateral Scanning Raman Scattering LiDAR