MLS Data Research Articles

Optimizing Bus Stop Environments: Analysis of Sun Glare Reduction with Green Elements in MLS and GPR Data

Optimizing Bus Stop Environments: Analysis of Sun Glare Reduction with Green Elements in MLS and GPR Data

Combining Cylindrical Voxel and Mask R-CNN for Automatic Detection of Water Leakages in Shield Tunnel Point Clouds

Water leakages can affect the safety and durability of shield tunnels, so rapid and accurate identification and diagnosis are urgently needed. However, current leakage detection methods are mostly based on mobile LiDAR data, making it challenging to detect leakage damage in both mobile and terrestrial LiDAR data simultaneously, and the detection results are not intuitive. Therefore, an integrated cylindrical voxel and Mask R-CNN method for water leakage inspection is presented in this paper. This method includes the following three steps: (1) a 3D cylindrical-voxel data organization structure is constructed to transform the tunnel point cloud from disordered to ordered and achieve the projection of a 3D point cloud to a 2D image; (2) automated leakage segmentation and localization is carried out via Mask R-CNN; (3) the segmentation results of water leakage are mapped back to the 3D point cloud based on a cylindrical-voxel structure of shield tunnel point cloud, achieving the expression of water leakage disease in 3D space. The proposed approach can efficiently detect water leakage and leakage not only in mobile laser point cloud data but also in ground laser point cloud data, especially in processing its curved parts. Additionally, it achieves the visualization of water leakage in shield tunnels in 3D space, making the water leakage results more intuitive. Experimental validation is conducted based on the MLS and TLS point cloud data collected in Nanjing and Suzhou, respectively. Compared with the current commonly used detection method, which combines cylindrical projection and Mask R-CNN, the proposed method can achieve water leakage detection and 3D visualization in different tunnel scenarios, and the accuracy of water leakage detection of the method in this paper has improved by nearly 10%.

Accurate and complete line segment extraction for large-scale point clouds

Line segment extraction from point clouds is critical for analyzing and understanding large-scale scenes. The main challenge is to generate line segments accurately as well as completely. However, state-of-the-art approaches continue to struggle with this issue. In this paper, we propose a novel method for effectively generating line segments from large-scale point clouds. To this end, we design a weighted centroid displacement scheme for identifying comprehensive feature points. Then, we employ an L1-median optimization to refine the identified feature points to perceive geometric edges on the underlying surface accurately. Following that, we generate complete and concise line segments from the refined feature points by designing three geometric operators: clustering, exclusion, and assimilation. The clustering operator generates the initial line segments based on optimized feature points, and the exclusion operator and the assimilation operator ensure the completeness and continuity of these line segments. We evaluate our approach on various scene point clouds, such as TLS, MLS, and ALS data. Extensive experimental results show that our method can outperform the competing approaches in terms of both accuracy and efficiency.

Mapping individual tree and plot-level biomass using airborne and mobile lidar in piñon-juniper woodlands

Piñon-juniper (PJ) woodlands are a widespread dryland ecosystem in the US containing an immense but poorly-constrained amount of aboveground biomass (AGB). Found at the dry end of the climatic range within which trees can persist, PJ faces an uncertain future in a changing climate, giving unique importance to mapping its AGB, past, present, and future. Lidar remote sensing offers great potential towards that end with research in a range of tree-dominant ecosystems demonstrating a strong capacity for mapping AGB. However, studies applying lidar to the task of mapping AGB in PJ are few. Given the unique structural characteristics of PJ trees, which tend to be short in stature (<10 m), possess multiple stems often obscured by low crown base heights, and irregularly-shaped canopies, there remains a high degree of uncertainty surrounding tree- and stand-level structural mapping in these woodlands. To resolve our limited understanding of how to best quantify AGB in PJ using lidar, we conducted a field-validated analysis comparing lidar platform (airborne laser scanner, ALS vs. mobile laser scanner, MLS) and analytical framework (area-based modeling vs. individual tree-based modeling). We used a random forest machine learning approach to predicting tree- and stand-level structure with leave-one-out cross-validation to assess model performance. In an area-based context, MLS only marginally outperformed ALS, with both able to explain approximately 80 % of variance in AGB. However, in an individual tree-based context, MLS significantly outperforms ALS at several tasks, including: (1) delineating tree crowns (MLS delineated 100 % of trees; ALS delineated 69 %); (2) directly characterizing tree structural parameters including height (R2ALS = 0.52 vs. R2MLS = 0.74) and crown area (R2ALS = 0.26 vs. R2MLS = 0.68); and (3) modeling AGB (R2ALS = 0.38 vs. R2MLS = 0.62). We attribute this difference in performance to the vastly greater structural detail contained within the MLS point cloud data as compared to ALS, with the former possessing an average of approximately 30,000 pts/m2 and the latter 14 pts/m2. Based on our results, we make the following recommendation: if individual tree structure is needed, MLS is the far superior option; however, given the wider availability, ALS data are a suitable substitute when mapping PJ AGB, particularly under an area-based modeling analytical framework. This study represents the first time MLS data have been applied to tree structure mapping in PJ, and provides strong quantitative evidence towards best structural and AGB mapping practices in this unique, widespread, and important ecosystem. It serves as a solid foundation upon which future studies aiming to map AGB in PJ and other similar dryland woodland ecosystems can be built.

Spatiotemporal Distribution of CO in the UTLS Region in the Asian Summer Monsoon Season: Analysis of MLS Observations and CMIP6 Simulations

In this study, CO is used as a tracer to evaluate the chemical field related to the Asian summer monsoon anticyclone (ASMA) in the upper troposphere and lower stratosphere (UTLS) region simulated by Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models from a multi-spatiotemporal perspective. The results show that the simulations of the six selected CMIP6 global climate models are well correlated with the MLS observations, while each model has its own advantages and disadvantages in the simulation of the ASMA and related chemical and geopotential height fields. Compared with MLS data, all six CMIP6 models can reasonably simulate the high CO values and the corresponding anticyclone, although certain biases exist in the simulations. Each model output has certain degrees of deviation in the simulation of the ASMA center position. In terms of time series, the six CMIP6 global models all exhibit an interannual variation CO mixing ratio over the ASM region while the interannual variation features are different from that in MLS. In general, it is impossible to identify a single determined model that can well reproduce the observations. In future work to assess the development trend and location of the ASMA, simulations of CESM2-WACCM and GFDL-ESM4 might be used due to their better performance than other models.

Ozone Anomalies in the Stratosphere of the Arctic and North Eurasia: Comparison of the 2011 and 2020 Events Using TEMIS and Aura MLS Data

Ozone Anomalies in the Stratosphere of the Arctic and North Eurasia: Comparison of the 2011 and 2020 Events Using TEMIS and Aura MLS Data

Neighborhood Heterogeneity: Implication to the Weighted Repeat Sales Method

ABSTRACT Does the degree of neighborhood heterogeneity, in and of itself, contribute to the cross-sectional price variations beyond that attributable to differences in physical characteristics within the neighborhood? This study presents theoretical and empirical investigations to confirm that the degree of property heterogeneity within the neighborhood can explain a significant portion of the cross-sectional price variations. Using the MLS data in south Florida, we conduct a series of regression analyses that yield consistent and robust results indicating that (1) neighborhood heterogeneity, whether structural or demographic, exhibits significant explanatory power to the pricing variations; (2) the market volatility, in contrast, only explains a small portion of the pricing variation, which confirms the finding of Case and Shiller; and (3) market liquidity, though not a neighborhood heterogeneity measure, does have a significant and negative effect on the pricing variation, suggesting that the availability of comparable sales can reduce pricing variation. These findings can potentially lead to an improvement over the widely-used Weighted Repeat Sales (WRS) method for constructing home price indices.

Detection and reconstruction of static vehicle-related ground occlusions in point clouds from mobile laser scanning

Vehicle-related ground occlusion is a common problem in MLS data. This study aims to design a detection and reconstruction method of static vehicle-related ground occlusion for MLS data. Ground extraction and vehicle segmentation are performed on the input point cloud data in advance. Then an α-shape boundary based on the prior vehicle geometry is designed to split non-ground empty area and ground occlusions. The occlusion is detected and matched with its corresponding vehicle using the relative position between them. This relative position relation and the height difference are used to detect the curb direction as the local road direction. Finally, the occlusions are reconstructed using two different methods: (1) a cell-based linear interpolation and (2) a point-based mathematical morphology. The methodology is tested by original scanned data and multi-temporal evaluation data captured from a residential area in Delft, the Netherlands with vehicle-mounted LiDAR sensors. The result shows that all occlusions cause by vehicles are successfully detected and the curb (road) direction is correctly extracted in most of the occluded areas. Both reconstructed results can visually integrate the original scanned data and recover the curb structure. The reconstruction errors of the linear interpolation method are 0.045 m in the z-axis direction and 0.051 m in total and the reconstruction errors of mathematical morphology are 0.048 m in the z-axis direction and 0.052 m in total.

The Annual Cycle in Mid-Latitude Stratospheric and Mesospheric Ozone Associated with Quasi-Stationary Wave Structure by the MLS Data 2011–2020

The purpose of this work is to study quasi-stationary wave structure in the mid-latitude stratosphere and mesosphere (40–50°N) and its role in the formation of the annual ozone cycle. Geopotential height and ozone from Aura MLS data are used and winter climatology for January–February 2011–2020 is considered. The 10-degree longitude segment centered on Longfengshan Brewer station (44.73°N, 127.60°E), China, is examined in detail. The station is located in the region of the Aleutian Low associated with the quasi-stationary zonal maximum of total ozone. Annual and semi-annual oscillations in ozone using units of ozone volume mixing ratio and concentration, as well as changes in ozone peak altitude and in time series of ozone at individual pressure levels between 316 hPa (9 km) and 0.001 hPa (96 km) were compared. The ozone maximum in the vertical profile is higher in volume mixing ratio (VMR) values than in concentration by about 15 km (5 km) in the stratosphere (mesosphere), consistent with some previous studies. We found that the properties of the annual cycle are better resolved in the altitude range of the main ozone maximum: middle–upper stratosphere in VMR and lower stratosphere in concentration. Both approaches reveal annual and semi-annual changes in the ozone peak altitudes in a range of 4–6 km during the year. In the lower-stratospheric ozone of the Longfengshan domain, an earlier development of the annual cycle takes place with a maximum in February and a minimum in August compared to spring and autumn, respectively, in zonal means. This is presumably due to the higher rate of dynamical ozone accumulation in the region of the quasi-stationary zonal ozone maximum. The “no-annual-cycle” transition layers are found in the stratosphere and mesosphere. These layers with undisturbed ozone volume mixing ratio are of interest for more detailed future study.

Ozone anomalies in the stratosphere of the Arctic and northern Eurasia: Comparison of 2011 and 2020 events using TEMIS and Aura MLS data

Ozone anomalies in the stratosphere of the Arctic and northern Eurasia: Comparison of 2011 and 2020 events using TEMIS and Aura MLS data

Characterizing ozone throughout the atmospheric column over the tropical Andes from in situ and remote sensing observations

In this study, we characterize atmospheric ozone over the tropical Andes in the boundary layer, the free troposphere, and the stratosphere; we quantify each contribution to total column ozone, and we evaluate the performance of the multi-sensor reanalysis (MSR2) in the region. Thus, we present data taken in Ecuador and Peru (2014–2019). The contribution from the surface was determined by integrating ozone concentrations measured in Quito and Cuenca (Ecuador) up to boundary layer height. In addition, tropospheric and stratospheric column ozone were quantified from ozone soundings (38) launched from Quito during the study time period. Profiles were compared against soundings at Natal (SHADOZ network) for being the closest observational reference with sufficient data in 2014–2019. Data were also compared against stratospheric mixing ratios from the Aura Microwave Limb Sounder (Aura MLS). Findings demonstrate that the stratospheric component of total column ozone over the Andes (225.2 ± 8.9 Dobson Units [DU]) is at similar levels as those observed at Natal (223.3 ± 8.6 DU), and observations are comparable to Aura MLS data. In contrast, the tropospheric contribution is lower over the Andes (20.2 ± 4.3 DU) when compared to Natal (35.4 ± 6.4 DU) due to a less deep and cleaner troposphere. From sounding extrapolation of Quito profiles down to sea level, we determined that altitude deducts about 5–7 DU from the total column, which coincides with a 3%–4% overestimation of the MSR2 over Quito and Marcapomacocha (Peru). In addition, when MSR2 data are compared along a transect that crosses from the Amazon over Quito, the Ecuadorian coast side, and into the Pacific, observations are not significantly different among the three first locations. Results point to coarse reanalysis resolution not being suitable to resolve the formidable altitude transition imposed by the Andes mountain chain. This work advances our knowledge of atmospheric ozone over the study region and provides a robust time series of upper air measurements for future evaluations of satellite and reanalysis products.

MULTISPECTRAL AND MOBILE MAPPING ISPRS WG III/5 DATA SET: FIRST ANALYSIS OF THE DATASET IMPACT

Abstract. Light Detection and Ranging (LiDAR) technology is playing a major role in different applications. Indeed, the possibility of exploiting either 3D geometric features and radiometric information makes LiDAR suitable for a wide range of practical domains. LiDAR proved also being quite flexible in terms of platforms where it can be implemented for the acquisition, spanning from airborne sensors up to car-based and hand-held instruments. Due to the rapid expansion of research concerning LiDAR intensity, the ISPRS WG III/5 launched in 2017 an initiative aimed at providing free access to LiDAR data acquired by modern multispectral ALS sensors as well as MLS data. The two datasets, MLS Data Set 1 – (“Sun Prairie”), and Multispectral LiDAR Data Set 2 – (“Tobermory”), were provided by Teledyne Optech Company (Canada) and were made freely available to researchers upon request. This paper is presenting the first results of this initiative in terms of applications, application domains and topics tackled by applicants. The relevance of this data set is also evaluated through a bibliometric analysis considering both Scopus and Web of Science indexed databases to analyse the main directions where the scientific research, the technical development and the application interest is moving to.

Extraction of road boundary from MLS data using laser scanner ground trajectory

Abstract Various means of extracting road boundary from mobile laser scanning data based on vehicle trajectories have been investigated. Independent of positioning and navigation data, this study estimated the scanner ground track from the spatial distribution of the point cloud as an indicator of road location. We defined a typical edge block consisting of multiple continuous upward fluctuating points by abrupt changes in elevation, upward slope, and road horizontal slope. Subsequently, such edge blocks were searched for on both sides of the estimated track. A pseudo-mileage spacing map was constructed to reflect the variation in spacing between the track and edge blocks over distance, within which road boundary points were detected using a simple linear tracking model. Experimental results demonstrate that the ground trajectory of the extracted scanner forms a smooth and continuous string just on the road; this can serve as the basis for defining edge block and road boundary tracking algorithms. The defined edge block has been experimentally verified as highly accurate and strongly noise resistant, while the boundary tracking algorithm is simple, fast, and independent of the road boundary model used. The correct detection rate of the road boundary in two experimental data is more than 99.2%.

INFLUENCE OF TIE POINTPLACEMENT DENSITY ON MOBILE LASER SCANNING DATA ADJUSTMENT RESULTS

The main stage of the mobile laser scanning data processing results, affecting the total accuracy of creating the final product, is the relative adjustment of trajectories and point clouds obtained during the re-passage of the survey system over the same area. The MLS data adjustment is performed based on the search for tie points in the overlapping areas of the driveways. Corresponding points can be added both interactively and automatically found using various algorithms and techniques. The accuracy of the adjustment depends on the reliability of the tie point identification, their number and the algorithm for searching local corrections to the position of the laser points. The study of the adjustment results depending on these factors is performed. It is shown how the selected adjustment algorithm and its parameters affect the final accuracy.

An Over-Segmentation-Based Uphill Clustering Method for Individual Trees Extraction in Urban Street Areas From MLS Data

In this article, an over-segmentation-based uphill clustering method for individual extraction of urban street trees from mobile laser scanning data is proposed to solve the problem that the existing methods depend heavily on tree trunks and have poor extraction results in complex environments where the tree trunks are blocked by cars and green belts, and the crown touching or interlocking is large. First, supervoxels are generated by over-segmentation, so that the amount of original data is reduced and the boundaries of different objects are effectively preserved. Then, the potential tree crowns and trunks are obtained by extracting typical object structures. Finally, individual trees extraction is realized by extracting independent crowns from the potential crowns via uphill clustering and searching corresponding trunks from the potential trunks. The main contribution of this article is to propose an individual extraction method for street trees based on uphill clustering that does not rely on the extraction of tree trunks, which improves the completeness of extracted results in complex urban environments. The experimental results demonstrate that the proposed method effectively extracted the street trees individually from the test data, with the completeness of 100%, the correctness of 96.4%, and the F-score of 0.98. Moreover, the proposed method also achieves good result for the extraction of greening trees that are heavily blocked in the green belt areas. And the corresponding completeness, correctness, and the F-score are 94.6%, 83.3%, and 0.89, respectively.

The use of mobile lidar data and Gaofen-2 image to classify roadside trees

Roadside trees are a vital component of urban greenery and play an important role in intelligent transportation and environmental protection. Quickly and efficiently identifying the spatial distribution of roadside trees is key to providing basic data for urban management and conservation decisions. In this study, we researched the potential of data fusing the Gaofen-2 (GF-2) satellite imagery rich in spectral information and mobile light detection and ranging (lidar) system (MLS) high-precision three-dimensional data to improve roadside tree classification accuracy. Specifically, a normalized digital surface model (nDSM) was derived from the lidar point cloud. GF-2 imagery was fused with an nDSM at the pixel level using the Gram–Schmidt algorithm. Then, samples were set including roadside tree samples from lidar data extracted by random sample consensus and other objects samples from field observation using the Global Positioning System. Finally, we conducted a segmentation process to generate an object-based image and completed the roadside tree classification at object level based on a support vector machine classifier using spectral features and completed local binary pattern (CLBP) texture features. Results show that classification using GF-2 alone and using nDSM alone result in 67.34% and 69.39% overall accuracy respectively with serious misclassification. The fusion image based on GF-2 and nDSM yields 77.55% overall accuracy. This means that the fusion of multi-source data is a great improvement over individual data. After adding the CLBP texture feature to the classification procedure, the classification accuracy of the fusion image is increased to 87.76%. The addition of CLBP texture features can clearly reduce the noise . Our results indicate that the classification of urban roadside trees can be realized by the fusion of satellite data and mobile lidar data with CLBP texture feature using the target-based classification method. Results also suggest that MLS data and CLBP texture features have the potential to effectively and efficiently improve the accuracy of satellite remote sensing classification.

Microwave Observations of Atmospheric Ozone above Nizhny Novgorod in the Winter of 2017–2018

We present the results of the ground-based microwave measurements of evolution of the vertical ozone content in the middle atmosphere above Nizhny Novgorod (56°20' N, 44° E) in the winter of 2017–2018. The results are compared with the satellite-based sensing data obtained using the MLS instrument onboard the Aura satellite and the ERA5 reanalysis data. In particular, the degree of the dependence of the stratospheric-ozone content on the position of the polar-vortex boundary with respect to the observation point at different altitudes is established. It is recorded that in January 2018 the vortex approached the city of Nizhny Novgorod and the boundary of the vortex up to its collapse (February 12), oscillated above the city, so that different altitude levels alternately appeared inside or outside the vortex. Such dynamics of the vortex most noticeably affected the evolution of the stratospheric maximum of the ozone content, whose position followed variation in the vortex boundary and quasiperiodically varied in the altitude range 30–35 km. The results of the ground-based microwave sensing give on the average a lower relative ozone content than the MLS data with a maximum systematic difference of about 0.8 ppm at the altitudes 38–39 km. Nevertheless, we recorded a more distinct response of the ozone content to a change in the structure of the vortex above Nizhny Novgorod than that in the case of using the satellite and reanalysis data.

Some features of the day-to-day MLT wind variability in winter 2017–2018 as seen with a European/Siberian meteor radar network

We present results of wind measurements near the mesopause carried out with meteor radars (MRs) at Collm (51°N, 13°E), Obninsk (55°N, 37°E), Kazan (56°N, 49°E), Angarsk (52°N, 104°E) and Anadyr (65°N, 178°E) from October 1, 2017 till March 31, 2018. The Collm and Kazan MRs are SKiYMET radars with vertical transmission and radio echo height finding, while the other radars operate with horizontal transmission and without height finding. We paid particular attention to the meridional wind variability with periods of 4–6 days and 9–11 days. The waves with these periods are seen as spots of the wave activity in the wavelet spectra and include oscillations with different periods and different discrete zonal wavenumbers. These wave packets successively propagate as a group of waves from one site to another one in such a way that they are observed at one site and almost disappear at the previous one. The 4–6 wave group includes planetary-scale oscillations (individual spectral components) which have eastward phase velocities and mostly zonal wavenumbers 2 and 3, and the vertical wavelength exceeds 70 km at middle latitudes. The source of the oscillations is the polar jet instability. The wave group itself propagates westward, and the amplitudes of wind oscillations are approximately 5–6 m/s as obtained from the wind data averaged over the meteor zone. The 9–11 day wave set propagates westward as a group and mainly consists of spectral components which have westward phase velocity and zonal wavenumber 1. Amplitudes of these wind perturbations strongly vary from station to station and can reach, approximately, 8 m/s. The vertical wavenumber is 0.014 km−1 as taken from the Kazan and 0.05 km−1 according to the Collm data. We obtained a global view on the waves by using the AURA MLS geopotential data. We found a good correspondence between wave features obtained from the MR wind measurements and the MLS data. To our knowledge, such a wave propagation of planetary wave in the mesosphere/lower thermosphere (MLT) region has so far not obtained much attention.

Quantifying the global impact of tropical cyclone‐associated gravity waves using HIRDLS, MLS, SABER and IBTrACS data

Tropical convective systems are major sources of atmospheric gravity waves (GWs). These waves are a key driver of global atmospheric circulation, especially in the middle and upper atmosphere. Tropical cyclones (TCs) such as hurricanes and typhoons are particularly dramatic examples of such systems, and are therefore potentially significant individual sources of GWs. To investigate this effect, GW observations from three satellite limb sounders in the vicinity of TCs are produced and analysed. By statistically combining 15 years of GW observations from 1,379 individual TCs represented in the International Best Track Archive for Climate Stewardship, it is shown that TCs are associated with a 15% increase of GW amplitudes over background and a 25% increase in measured momentum fluxes, primarily during the period immediately before the TC. It is further shown that this additional contribution is small relative to other GW‐generating processes, and thus that individual TCs do not have a large quantitative effect on the dynamics of the middle and upper atmosphere as a whole. Thus, it is concluded that accurate modelling of TC‐generated short vertical wavelength GWs need not be a development priority for the next generation of weather and climate models. The results also demonstrate that stronger GW activity is associated with TCs that will later develop into hurricane‐intensity storms than is observed for those that will not, and thus that better space‐based monitoring of stratospheric GW activity could be a useful tool to help improve the forecasting of strong hurricane events in the presence of obscuring tropospheric cloud.

Evaluation of CESM1 (WACCM) free-running and specified dynamics atmospheric composition simulations using global multispecies satellite data records

Evaluation of CESM1 (WACCM) free-running and specified dynamics atmospheric composition simulations using global multispecies satellite data records