Ground-based Light Detection And Ranging Research Articles

Validation of wind measurements from a multirotor RPAS-mounted ultrasonic wind sensor using a ground-based LiDAR system

The aim of this research was to establish the validity of wind measurements from on board a multirotor Remotely Piloted Aircraft System (RPAS) for the purposes of wind monitoring applications. A custom-built hexacopter RPAS recorded wind speed and direction by means of an onboard ultrasonic wind sensor, whilst operating in the inherently highly stochastic nature of open field atmospheric conditions. Experimental data were collected during open field hovering flights subject to different ambient conditions with free stream horizontal wind speeds reaching up to 12 m/s. Flights were conducted at different altitudes above ground level and in proximity to a Light Detection and Ranging (LiDAR) remote wind measurement unit that was used as a low-resolution reference meteorological station. Very good correlation was obtained between the RPAS and LiDAR unit for both wind speed and wind direction measurements across all hovering flight altitudes. The RPAS-based wind speed measurements were found to have a consistent 1 m/s positive offset, whilst the RPAS-based wind direction readings had a 6.16° negative offset. These were potentially caused by differences in the localized wind fields between the LiDAR and RPAS measuring positions, as well as by localized RPAS rotor-induced air flows for wind speed measurements and potential slight misalignments in the instruments’ reference datum for wind direction readings.

Morphometric constraints on the formation of new terrestrial analogs for planetary pits

The origin of geological depressions abounding on Mars and other planetary bodies remains poorly understood, partially due to the limited variability in the geological settings of existing terrestrial analogs. Here, we present a new terrestrial analog that is located at the northwestern margin of the Levantine volcanic field of Harrat Ash−Shaam along the Dead Sea Transform. The analog site consists of tens of geological depressions (locally named “juba”) that morphologically resemble Martian bowl-shaped pits and occur within a Pleistocene basaltic plateau that overlies Meso-Cenozoic carbonates. To constrain plausible formation mechanisms for the juba depressions, we carried out detailed field mapping and morphometric analyses using a 0.25 m/pixel digital terrain model (DTM) derived from airborne light detection and ranging (LiDAR) survey covering 34 km2 of the study area, and centimeter-scale, ground-based LiDAR scans of selected juba depressions. We show that variable magnitudes of slope asymmetry between north- and south-facing walls within the juba depressions, along with different degrees of sediment infilling, provide effective proxies for the relative geomorphic maturity of these landforms, and in turn indicate asynchronous formation of the juba depressions after the Pleistocene emplacement of the Harrat Ash−Shaam basalts in the study area. Our findings preclude formation of the juba depressions by phreatomagmatic explosions and instead point toward collapse into missing subsurface volume. In a broader context, we propose that the morphometric analyses developed herein to distinguish between plausible juba formation mechanisms in the Harrat Ash−Shaam volcanic field can be extended to better constrain the formation mechanisms of similar pit features on Mars and other planetary bodies.

Tree Species Classification Using Ground-Based LiDAR Data by Various Point Cloud Deep Learning Methods

Tree species information is an important factor in forest resource surveys, and light detection and ranging (LiDAR), as a new technical tool for forest resource surveys, can quickly obtain the 3D structural information of trees. In particular, the rapid and accurate classification and identification of tree species information from individual tree point clouds using deep learning methods is a new development direction for LiDAR technology in forest applications. In this study, mobile laser scanning (MLS) data collected in the field are first pre-processed to extract individual tree point clouds. Two downsampling methods, non-uniform grid and farthest point sampling, are combined to process the point cloud data, and the obtained sample data are more conducive to the deep learning model for extracting classification features. Finally, four different types of point cloud deep learning models, including pointwise multi-layer perceptron (MLP) (PointNet, PointNet++, PointMLP), convolution-based (PointConv), graph-based (DGCNN), and attention-based (PCT) models, are used to classify and identify the individual tree point clouds of eight tree species. The results show that the classification accuracy of all models (except for PointNet) exceeded 0.90, where the PointConv model achieved the highest classification accuracy for tree species classification. The streamlined PointMLP model can still achieve high classification accuracy, while the PCT model did not achieve good accuracy in the tree species classification experiment, likely due to the small sample size. We compare the training process and final classification accuracy of the different types of point cloud deep learning models in tree species classification experiments, further demonstrating the advantages of deep learning techniques in tree species recognition and providing experimental reference for related research and technological development.

Ground-based/UAV-LiDAR data fusion for quantitative structure modeling and tree parameter retrieval in subtropical planted forest

Light detection and ranging (LiDAR) has contributed immensely to forest mapping and 3D tree modelling. From the perspective of data acquisition, the integration of LiDAR data from different platforms would enrich forest information at the tree and plot levels. This research develops a general framework to integrate ground-based and UAV-LiDAR (ULS) data to better estimate tree parameters based on quantitative structure modelling (QSM). This is accomplished in three sequential steps. First, the ground-based/ULS LiDAR data were co-registered based on the local density peaks of the clustered canopy. Next, redundancy and noise were removed for the ground-based/ULS LiDAR data fusion. Finally, tree modeling and biophysical parameter retrieval were based on QSM. Experiments were performed for Backpack/Handheld/UAV-based multi-platform mobile LiDAR data of a subtropical forest, including poplar and dawn redwood species. Generally, ground-based/ULS LiDAR data fusion outperforms ground-based LiDAR with respect to tree parameter estimation compared to field data. The fusion-derived tree height, tree volume, and crown volume significantly improved by up to 9.01%, 5.28%, and 18.61%, respectively, in terms of r RMSE. By contrast, the diameter at breast height (DBH) is the parameter that has the least benefits from fusion, and r RMSE remains approximately the same, because stems are already well sampled from ground data. Additionally, particularly for dense forests, the fusion-derived tree parameters were improved compared to those derived from ground-based LiDAR. Ground-based LiDAR can potentially be used to estimate tree parameters in low-stand-density forests, whereby the improvement owing to fusion is not significant. • Mitigation of the limitations of tree localization in case of ground-based/ULS LiDAR data co-registration using virtual keypoints. • Fusion of ground-based/ULS LiDAR data based on removal of redundant and noisy points. • Investigation and evaluation of the utility of ground-based/ULS LiDAR data fusion for tree parameter estimation. • Ground-based/ULS LiDAR data fusion improved estimation of tree height, tree volume, and crown volume. • Ground-based/ULS LiDAR data fusion has strong potential in QSM of trees particularly in high-stand-density forest.

A Model Study of Building Seismic Damage Information Extraction and Analysis on Ground-Based LiDAR Data

Earthquake disasters can have a serious impact on people’s lives and property, with damage to buildings being one of the main causes of death and injury. A rapid assessment of the extent of building damage is essential for emergency response management, rescue operations, and reconstruction. Terrestrial laser scanning technology can obtain high precision light detection and ranging (LiDAR) point cloud data of the target. The technology is widely used in various fields; however, the quantitative analysis of building seismic information is the focus and difficulty of ground-based LiDAR data analysis processing. This paper takes full advantage of the high-precision characteristics of ground-based LiDAR data. A triangular network vector model (TIN-shaped model) was created in conjunction with the alpha shapes algorithm, solving the problem of small, nonvisually identifiable postearthquake building damage feature extraction bias. The model measures the length, width, and depth of building cracks, extracts the amount of wall tilt deformation, and labels the deformation zone. The creation of this model can provide scientific basis and technical support for postearthquake emergency relief, assessment of damage to buildings, extraction of deformation characteristics of other structures (bridges, tunnels, dams, etc.), and seismic reinforcement of buildings. The research data in this paper were collected by the author’s research team in the first time after the 2013 Lushan earthquake and is one of the few sets of foundation of LiDAR data covering the full range of postearthquake building types in the region, with the data information mainly including different damage levels of different structural types of buildings. The modeling analysis of this data provides a scientific basis for establishing the earthquake damage matrix of buildings in the region.

Tree species classification of LiDAR data based on 3D deep learning

Accurate tree species identification is essential for ecological evaluation and other forest applications. In this paper, we proposed a point-based deep neural network called LayerNet. For light detection and ranging (LiDAR) data in forest regions, the network can divide multiple overlapping layers in Euclidean space to obtain the local three-dimensional (3D) structural features of the tree. The features of all layers are aggregated, and the global feature is obtained by convolution to classify the tree species. To validate the proposed framework, multiple experiments, including airborne and ground-based LiDAR datasets, are conducted and compared with several existing tree species classification algorithms. The test results show that LayerNet can directly use 3D data to accurately classify tree species, with the highest classification accuracy of 92.5%. Also, the results of comparative experiments demonstrate that the proposed framework has obvious advantages in classification accuracy and provides an effective solution for tree species classification tasks.

3D monitoring of Paleolithic archaeological excavations using terrestrial laser scanner systems (Sierra de Atapuerca, Railway Trench sites, Burgos, N Spain)

The advantages of active massive data capture devices such as LiDAR (Light Detection And Ranging) in the creation of high-resolution topographic models have been well known for years. They have been widely applied to the documentation of cultural heritage since the beginning of the 21st century. As LiDAR devices, terrestrial laser scanning (TLS) systems, or ground-based LiDAR, have shown themselves since then to have a multitude of applications and have proven clear advantages over classical survey methods. Nevertheless, few attempts have been made to use this method on Paleolithic archaeological excavations beyond one-time 3D reconstructions, which are mainly focused on forming a basis for conducting other studies. Since the excavation process involves the irreversible modification or destruction of deposits and spatial relationships that will not be reproduced again, the comprehensive reconstruction of excavation surfaces represents a fundamental issue for the research, dissemination, and conservation of Paleolithic sites. Through this work, we show the techniques and potential of the sequential geometric documentation of Paleolithic site excavations using TLS systems on the Sierra de Atapuerca Railway Trench sites (Gran Dolina, Galería and Sima del Elefante). Related to this sequential documentation, a case of application and a storage concept in Paleolithic archaeology is presented. As an application, the models were used to monitor the excavation surface changes and physical damage to excavation sections occurring during the last seven years. As a concept, multi-temporal models and derived information were stored in a database from which their design is displayed, the 4D (i.e., time dimension added to the three-dimensional spatial data) database. The results reveal TLS systems, 4D data, and 4D databases as efficient methods for providing detailed reconstructions, exhaustive change models, and storage of paleosurfaces, which could be applied also to other multi-annual excavation sites.

Vertical distributions of tropospheric formaldehyde, nitrogen dioxide, ozone and aerosol in southern China by ground-based MAX-DOAS and LIDAR measurements during PRIDE-GBA 2018 campaign

As the development of megacities, combined air pollution has been of increasing concern in China, which is composed by primary pollution, such as Nitrogen dioxide (NO2), and the followed secondary pollution under certain meteorology circumstances and high concentration of precursors, such as tropospheric ozone, formaldehyde (HCHO) and fine particles. During PRIDE-GBA 2018 Campaign (Particles, Radicals, Intermediates from oxiDation of primary Emissions in Great Bay Area), we performed the high resolution vertical observations of pollution gases and aerosol, which are significant but scarce indexes in studying the mechanisms of tropospheric pollution, using remote sensing techniques. NO2 and HCHO column densities and vertical profiles are reconstructed from data of multi-axis differential optical absorption spectroscopy (MAX-DOAS). Tropospheric ozone profile and aerosol extinction are retrieved by the differential absorption light detection and ranging (LIDAR) system. In order to verifying the accuracy, we evaluated the consistency and reliability of MAX-DOAS and LIDAR measurements. The results showed reasonable agreement with in-situ and off-line observations at Canton Tower. A typical combined pollution process from Sep 29 to Oct 10 was analyzed in detail. More than 200 μg/m3 tropospheric ozone in this case was considered from the anthropogenic sources, including industry, power plant, residential and transportation. The high emission from local industry and polluted air transport from northeast brought the main precursors of this pollution process. The photochemical formation of tropospheric ozone was turned into a VOC-limited regime on polluted days.

HISTORY AND APPLICATIONS OF SPACE-BORNE LIDARS

Abstract. LIDAR (Light Detection and Ranging) is a laser altimeter system that determines the distance by measuring pulse travel time. The data from the LIDAR systems provide unique information on the vertical structure of land covers. Compared to ground-based and airborne LIDARs providing a high-resolution digital surface model, space-borne LIDARs can provide important information about the vertical profile of the atmosphere in a global scale. The overall objective of these satellites is to study the elevation changes and the vertical distribution of clouds and aerosols. In this paper an overview on the space-borne laser scanner satellites are accomplished and their applications are introduced. The first space-borne LIDAR is the ICESat (Ice, Cloud and land Elevation Satellite) satellite carrying the GLAS instrument which was launched in January 2003. The CALIPSO (the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations, 2006), CATS-ISS (the Cloud-Aerosol Transport System, 2015), ADM-Aeolus (Atmospheric Dynamics Mission, 2018), and ICESat-2 (Ice, Cloud and land Elevation Satellite-2, 2018) satellites were respectively lunched and began to receive information about the vertical structure of the atmosphere and land cover. In addition, two ACE (The Aerosol-Cloud-Ecosystems, 2022) and EarthCARE (Earth Clouds, Aerosols and Radiation Explorer, 2021) space-borne satellites were planned for future. The data of the satellites are increasingly utilized to improve the numerical weather predictions (NWP) and climate modeling.

Individual Rubber Tree Segmentation Based on Ground-Based LiDAR Data and Faster R-CNN of Deep Learning

Rubber trees in southern China are often impacted by natural disturbances that can result in a tilted tree body. Accurate crown segmentation for individual rubber trees from scanned point clouds is an essential prerequisite for accurate tree parameter retrieval. In this paper, three plots of different rubber tree clones, PR107, CATAS 7-20-59, and CATAS 8-7-9, were taken as the study subjects. Through data collection using ground-based mobile light detection and ranging (LiDAR), a voxelisation method based on the scanned tree trunk data was proposed, and deep images (i.e., images normally used for deep learning) were generated through frontal and lateral projection transform of point clouds in each voxel with a length of 8 m and a width of 3 m. These images provided the training and testing samples for the faster region-based convolutional neural network (Faster R-CNN) of deep learning. Consequently, the Faster R-CNN combined with the generated training samples comprising 802 deep images with pre-marked trunk locations was trained to automatically recognize the trunk locations in the testing samples, which comprised 359 deep images. Finally, the point clouds for the lower parts of each trunk were extracted through back-projection transform from the recognized trunk locations in the testing samples and used as the seed points for the region’s growing algorithm to accomplish individual rubber tree crown segmentation. Compared with the visual inspection results, the recognition rate of our method reached 100% for the deep images of the testing samples when the images contained one or two trunks or the trunk information was slightly occluded by leaves. For the complicated cases, i.e., multiple trunks or overlapping trunks in one deep image or a trunk appearing in two adjacent deep images, the recognition accuracy of our method was greater than 90%. Our work represents a new method that combines a deep learning framework with point cloud processing for individual rubber tree crown segmentation based on ground-based mobile LiDAR scanned data.

Using ground-based LiDAR to detect shoot dieback: a case study on Yunnan pine shoots

ABSTRACTOwing to the tiny, slim shape and clumping features of needles in a shoot, segmenting individual conifer needles or shoots using ground-based LiDAR (Light Detection and Ranging) is challenging. Very few measurements techniques or models have focused on the shoot point cloud. This letter presents a case study on detecting the dieback rate of individual shoots using LiDAR, which assessed the ability and sensitivity of LiDAR parameters to detect shoot shape and dieback rate. First, typical three-dimensional (3D) models of pine shoots were generated. Second, the waveform simulation model used in large footprint LiDAR was modified to simulate the small footprint LiDAR discretized returns on shoots. Third, a case study on Yunnan pine (Pinus yunnanensis) shoots was conducted to evaluate measurements using the Velodyne VLP-16 (Velodyne LiDAR Puck LITE-16) LiDAR. Finally, for the detection of shoot dieback, an expanded sensitivity analysis was performed on LiDAR beam divergence, angle resolution, and distance. The results suggest that it is nearly impossible to obtain 3D shape signatures of individual needles; however, shoot dieback rate can be extracted if mean intensity and point number features are properly used.

Rubber Tree Crown Segmentation and Property Retrieval Using Ground-Based Mobile LiDAR after Natural Disturbances

Rubber trees in southern China are often impacted by natural disturbances, and accurate rubber tree crown segmentation and property retrieval are of great significance for forest cultivation treatments and silvicultural risk management. Here, three plots of different rubber tree clones, PR107, CATAS 7-20-59 and CATAS 8-7-9, that were recently impacted by hurricanes and chilling injury were taken as the study targets. Through data collection using ground-based mobile light detection and ranging (LiDAR) technology, a weighted Rayleigh entropy method based on the scanned branch data obtained from the region growing algorithm was proposed to calculate the trunk inclination angle and crown centre of each tree. A watershed algorithm based on the extracted crown centres was then adopted for tree crown segmentation, and a variety of tree properties were successfully extracted to evaluate the susceptibility of different rubber tree clones facing natural disturbances. The results show that the angles between the first-order branches and trunk ranged from 35.1–67.7° for rubber tree clone PR107, which is larger than the angles for clone CATAS 7-20-59, which ranged from 20.2–43.2°. Clone PR107 had the maximum number of scanned leaf points, lowest tree height and a crown volume that was larger than that of CATAS 7-20-59, which generates more frontal leaf area to oppose wind flow and reduces the gaps among tree crowns, inducing strong wind loading on the tree body. These factors result in more severe hurricane damage, resulting in trunk inclination angles that are larger for PR107 than CATAS 7-20-59. In addition, the rubber tree clone CATAS 8-7-9 had the minimal number of scanned leaf points and the smallest tree crown volume, reflecting its vulnerability to both hurricanes and chilling injury. The results are verified by field measurements. The work quantitatively assesses the susceptibility of different rubber tree clones under the impacts of natural disturbances using ground-based mobile LiDAR.

Multi-frequency differential absorption LIDAR system for remote sensing of CO2 and H2O near 1.6 µm.

The specifications and performance of a ground-based differential absorption LIDAR (light detection and ranging) system (DIAL) using an optical parametric oscillator (OPO) are presented. The OPO is injection-seeded with the output of a confocal filter cavity at frequencies generated by an electro-optic phase modulator (EOM) from a fixed-frequency external cavity diode laser (ECDL). The number of seed frequencies, frequency spacings, and duration is controlled with an arbitrary waveform generator (AWG) driving the EOM. Range resolved data are acquired using both photon current and photon counts from a hybrid detection system. The DIAL measurements are performed using a repeating sequence of 10 frequencies spanning a range of 37.5 GHz near 1602.2 nm to sequentially sample CO2 and H2O at 10 Hz. Dry air mixing ratios of CO2 and H2O with a resolution of 250 m and an averaging time of 10 min resulted in uncertainties as low as 6 µmol/mol (ppm) and 0.44 g/kg, respectively. Simultaneous measurements using an integrated path differential absorption (IPDA) LIDAR system and in situ point sensor calibrated to WMO (World Meteorological Organization) gas standards are conducted over two 10 hr nighttime periods to support traceability of the DIAL results. The column averaged DIAL mixing ratios agree with the IPDA LIDAR results to within the measured uncertainties for much of two measurement periods. Some of the discrepancies with the in situ point sensor results are revealed through trends observed in the gradients of the range resolved DIAL data.

Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model

The use of active remote sensing techniques based on light detection and ranging (LiDAR) was investigated here to estimate the green area index (GAI) of wheat crops. Emphasis was put on the maximum GAI development stage when saturation effects are known to limit the performances of standard indirect methods based either on the gap fraction or reflectance measurements. The LiDAR provides both the three dimensional (3D) point cloud from which the vertical distribution (Z profile) of the interception points is computed, as well as the intensity of the returned signal from which the green fraction (GF) is derived. The data were interpreted by exploiting the 3D ADEL-Wheat model that synthesizes the knowledge accumulated on wheat canopy structure. A LiDAR simulator that accounts for the specific observation configuration used was developed to mimic the actual LiDAR measurements. The in-silico experiments were conducted to generate training and validation dataset. Neural network were then used to estimate GAI from the Z profile and GF derived from the LiDAR measurements. Performances of GAI estimates by the several methods investigated were evaluated using either experimental data with 3<GAI<6 and data simulated with the 3D structure model with 1<GAI<7.Results confirm that using only the GF provides poor estimates of GAI (0.89<RMSE<1.28; 0.22<rRMSE<0.31), regardless of turbid medium or realistic assumptions on canopy 3D structure. The introduction of the Z profile information improved significantly the GAI estimation accuracy (0.48<RMSE<0.55; 0.12<rRMSE<0.13). This study demonstrates the interest of using the third dimension provided by LiDAR to better estimate GAI in crops under high GAI values. However, this requires the use of a realistic 3D structure crop model over which the LiDAR data could be simulated under the observational configuration used.

Utilizing ground-based LIDAR measurements to aid autonomous airdrop systems

Uncertainty in atmospheric winds represents one of the primary sources of landing error in airdrop systems. In this work, a ground-based LIDAR system samples the wind field at discrete points above the target and transmits real-time data to approaching autonomous airdrop systems. In simulation and experimentation, the inclusion of a light detection and ranging (LIDAR) system showed a maximum of 40% improvement over unaided autonomous airdrop systems. Wind information nearest ground level has the largest impact on improving accuracy.

Analysis and Verification of Wind Data from Ground-based LiDAR

A study on the verification of reliability of measurements by the LiDAR (Light Detection and Ranging) system was conducted in Kimnyeong, on Jeju Island, South Korea. Also, the accuracy of the LiDAR measurements taken from the relatively flat-terrained Kimnyeong site was determined after factoring in the wind disturbance caused by the wake behind wind turbines. The 2.5-month wind data collected by the LiDAR were compared with concurrent wind data collected by the conventional anemometry on a nearby 120 m-high met mast for the verification. The measurement sectors (the area around the wind turbine where undisturbed wind speeds were measured) and the disturbed sectors (the areas around the wind turbine prone to wind disturbance) were estimated in accordance with the International Electrotechnical commission, IEC 61400-12-1. Data filtering was performed based on the criteria suggested by previous research. As a result, at 116 m above ground level, comparatively high LiDAR error rates of 0.50∼12.69 % were found in the disturbed sector, while relatively low LiDAR error rates of -0.93∼4.26 % were shown in the measurement sector. In the measurement sector, the absolute values of LiDAR error rates were about 5 % with the standard deviations of about 5 % at the heights of 100 and 116 m above ground level. The power law exponents calculated by using the met mast and the LiDAR data collected from the measurement sector were very similar to each other, whereas met mast and LiDAR data collected from the disturbed sector showed major discrepancies.

A comparison of ground-based LiDAR and met mast wind measurements for wind resource assessment over various terrain conditions

In order to assess reliability of measurements from LiDAR (Light Detection and Ranging), a measurement campaign was led using ground-based LiDAR of WINDCUBE V2 and meteorological masts at three measurement sites: Sumang, Gangjeong, and Susan, on Jeju Island, Korea. Each site had a different topographical complexity, which was evaluated by using a Ruggedness Index (RIX). Wind data was collected for 11−14 days from four heights on each site's met mast. Data filtering was done to ensure data comparability between LiDAR and wind sensors. Analyses of LiDAR error, standard deviation, turbulence intensity and LiDAR error rate were conducted on data coming from each site. Also, the CFD analysis was performed at Sumang with the highest RIX. As a result, the concurrent wind measurement slopes were all close to one based on linear regression analysis. The coefficient of determination was almost all more than 0.9 for all heights at each site. LiDAR error rates for the measurement sites ranged approximately between 2% and 6%. The result of the CFD analysis showed that the depression was formed between two parasitic cones, between which the measurement point of Sumang was located, which led to greater positive LiDAR error.

Integrated application of a high-resolution LIDAR outcrop survey of an unconventional Niobrara Reservoir, Denver Basin, Colorado

The Niobrara Shale is a major reservoir in the Denver Basin. To understand the importance of natural fractures in this reservoir a dataset comprised of fracture plane orientations and fracture intensity variations using a ground-based LIDAR (Light Detection and Ranging) and photogrammetry was acquired. The purpose was to use an outcrop as an analog to the subsurface reservoir for improved reservoir characterization. Listric faults associated with negative flower structures show increased fracture intensity near the faults. Fracture sets remain consistent throughout the interbedded chalks, marls, and limestones. However, there is an apparent variability of fracture spacing associated with changes in lithology. Mapping the fracture orientation and intensity into the subsurface using the outcrop analog should give rise to better geomechanical and fluid flow modelling.

Terrestrial LiDAR Survey and Morphological Analysis to Identify Infiltration Properties in the Tamala Limestone, Western Australia

Caves are an ideal observatory of infiltration water in karstified limestone, and the application of remote sensing techniques can bring new insights toward flow patterns and processes. We present an exhaustive characterization of Golgotha Cave in SW Western Australia, based on a light detection and ranging (LiDAR) measurement campaign. The cave is developed in Quaternary age aeolianite (dune limestone) and its infiltration waters form speleothems. We collect ground-based LiDAR scans of the cave ceiling at three sites within the cave system. The resulting point-clouds are analyzed using mathematical morphology to determine statistical information on stalactite widths, lengths, and spatial distributions. We establish a relationship between stalactite diameter and length that is in agreement with the platonic ideal of stalactite shape. We relate stalactite density variation with topography of the cave ceiling and variations in hydraulic gradient. From this analysis, it appears that longer stalactites tend to occur in comparatively lower ceiling elevation, which, we hypothesize, represents greater mass of water in the limestone above the roof of the cave. We also investigate the relationship between stalactite distribution and ceiling features such as fractures. We apply this to identify different types of possible flow patterns such as matrix flow and fracture flow. This analysis demonstrates a spatial variability, with one site having linear groups of stalactites and another site mostly dominated by stalactite clusters.

Mie LIDAR Observations of Tropospheric Aerosol over Wuhan

Wuhan is a rapidly developing large city in central China. To analyze the aerosol characteristics over Wuhan, the optical properties of the nocturnal aerosol layers in the lower troposphere were observed using a ground-based LIDAR(Light Detection And Ranging) located in the Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) from Wuhan University, China (114°21′E, 30°32′N) in January 2013–January 2015. The vertical distribution and temporal variation of tropospheric aerosols over Wuhan were summarized. The atmospheric boundary layer height (ABLH) was mainly at an altitude of 1.5–2 km (~33.1% probability), with an annual average of 1.66 km. The ABLH was higher in spring–summer (~2 km) and lower in autumn–winter (~1.2 km). The aerosol optical depth (AOD) was higher in spring–autumn than in summer–winter. The highest AOD was about 0.79 in October and the lowest was about 0.08 in January. The annual average was about 0.3. To study the relationship between the AOD and the particulate matter ≤2.5 µm in the aerodynamic diameter (PM2.5) in the lower troposphere, a typical haze event from 9–14 October 2014 was investigated. The results showed a correlation coefficient of 0.5165 between these two variables.