Point cloud classification is the most important problem in airborne LiDAR point cloud data processing. In recent years, classification strategies with new theoretical background keep emerging, so it ...is urgent to make a more systematic and detailed summary of existing point cloud filtering algorithms, so that relevant researchers can have a more macroscopic and clear understanding of various algorithms and their advantages and disadvantages. Based on the characteristics of airborne LiDAR point cloud data, this paper combs the general process of point cloud classification. This paper summarizes the current mainstream classification methods and analyses their application effects in different scenarios, aiming at exploring and customizing suitable point cloud classification methods according to specific purpose objectives or industry standards. The point cloud classification is classified into three-level classification strategy. The first-level classification is gross error elimination, the second-level classification is point cloud filtering, which is to distinguish ground points from non-ground points. The third-level classification is to extract thematic point clouds from non-ground points according to application requirements. At present, the primary and secondary classification methods are relatively diverse and mature, reaching a certain level of application, while the tertiary classification is still in the initial stage of exploration, and large-scale application is not widespread.
Several studies have shown that most climate models underestimate cloud cover and overestimate cloud reflectivity, particularly for the tropical low‐level clouds. Here, we analyze the characteristics ...of low‐level tropical marine clouds simulated by six climate models, which provided COSP output within the CMIP6 project. CALIPSO lidar observations and PARASOL mono‐directional reflectance are used for model evaluation. It is found that the “too few, too bright” bias is still present for these models. The reflectance is particularly overestimated when cloud cover is low. Models do not simulate any optically thin clouds. They fail to reproduce the increasing cloud optical depth with increasing lower tropospheric stability as observed. These results suggest that most models do not sufficiently account for the effect of the small‐scale spatial heterogeneity in cloud properties or the variety of cloud types at the grid scale that is observed.
Plain Language Summary
Low‐level clouds are ubiquitous in the tropics and play an important role in Earth's radiative balance. Climate models do not explicitly resolve the main low‐level cloud formation processes, which must therefore be parameterized. This modeling work is difficult and in the previous generation of models low‐level clouds had a systematically too low fraction and too large brightness. This models' deficiency is known as the “too few too bright bias.” Here, we use six climate models of the latest generation that are compared to lidar and reflectance observations allowing for a detailed characterization of cloud properties. It is found that the too few too bright bias is still present for these models. Other common deficiencies in cloud simulation are revealed. At the daily time scale and models' grid scale, the lower the cloud cover, the greater the overestimation of the cloud brightness. Models do not simulate any thin clouds. They fail to reproduce the increasing cloud brightness with increasing stability of the lower troposphere as observed. The study suggests that most models do not sufficiently account for the variety of cloud properties and cloud types at the models' grid scale that is observed.
Key Points
The “too few too bright” bias is still present in six CMIP6 models for low‐level clouds
The overestimation of the low‐level cloud brightness gets higher as their cover is low
Models fail to reproduce the increasing cloud optical depth with increasing lower tropospheric stability as observed
The visual quality of urban streets is of vital importance for establishing a satisfying and comfortable experience for the residents in an urban community. It also has positive effects on urban ...vibrancy, public health, and social connections in that community. Numerous studies have been conducted to evaluate the visual quality at the urban street level using street view images. However, the spatial distribution of fine-grained visual quality inside an urban street is rarely investigated. This study presents a new approach for the evaluation of visual quality inside urban streets using mobile LiDAR point clouds. The semantic information of urban streets was first extracted from mobile LiDAR point clouds with a Gradient Boosting classifier. After that, seven well-known key design elements, including the green space factor, sky view factor, enclosure rate, volume index, vehicle occurrence rate, motorization rate, and diversity, were calculated from the classified point clouds using a three-dimensional (3D) visibility model. Finally, the visual quality at 1 m grid resolution inside urban street was achieved automatically by using a random forest model which was trained based on perception samples. This approach has been validated on two study areas and the results indicated that the proposed approach is able to quantitatively examine the visual quality difference inside urban streets. The results generated by the proposed method also match well with the common sense of urban design experts, which are useful for architects and designers to develop best practices in the urban micro-renewal project and to refine the urban planning processes.
•A mobile LiDAR data-based method is proposed for measuring street visual quality.•Seven key design elements were calculated in a 3D way using mobile point cloud data.•A random forest model is proposed for quantifying the fine-scale street visual quality.•Two visual quality maps at 1 m resolution were produced to investigate the spatial distribution of fine-grained visual quality inside urban streets.
Crop improvement efforts are targeting increased above-ground biomass and radiation-use efficiency as drivers for greater yield. Early ground cover and canopy height contribute to biomass production, ...but manual measurements of these traits, and in particular above-ground biomass, are slow and labor-intensive, more so when made at multiple developmental stages. These constraints limit the ability to capture these data in a temporal fashion, hampering insights that could be gained from multi-dimensional data. Here we demonstrate the capacity of Light Detection and Ranging (LiDAR), mounted on a lightweight, mobile, ground-based platform, for rapid multi-temporal and non-destructive estimation of canopy height, ground cover and above-ground biomass. Field validation of LiDAR measurements is presented. For canopy height, strong relationships with LiDAR (
of 0.99 and root mean square error of 0.017 m) were obtained. Ground cover was estimated from LiDAR using two methodologies: red reflectance image and canopy height. In contrast to NDVI, LiDAR was not affected by saturation at high ground cover, and the comparison of both LiDAR methodologies showed strong association (
= 0.92 and slope = 1.02) at ground cover above 0.8. For above-ground biomass, a dedicated field experiment was performed with destructive biomass sampled eight times across different developmental stages. Two methodologies are presented for the estimation of biomass from LiDAR: 3D voxel index (3DVI) and 3D profile index (3DPI). The parameters involved in the calculation of 3DVI and 3DPI were optimized for each sample event from tillering to maturity, as well as generalized for any developmental stage. Individual sample point predictions were strong while predictions across all eight sample events, provided the strongest association with biomass (
= 0.93 and
= 0.92) for 3DPI and 3DVI, respectively. Given these results, we believe that application of this system will provide new opportunities to deliver improved genotypes and agronomic interventions via more efficient and reliable phenotyping of these important traits in large experiments.
The effective fusion of multisource data helps to improve the performance of land cover classification. Most existing convolutional neural network (CNN)-based methods adopt an early/late fusion ...strategy to fuse the low-/high-level features for classification, which still has two inherent challenges: 1) the conventional convolution operation performs a weighted average operation on each pixel in the receptive field, which will reduce the discriminability of the center pixel due to the influence of the interference pixels and 2) the spatial–spectral features of the hyperspectral image (HSI), the elevation features of light detection and ranging (LiDAR), and the complementary features between the multimodal data are not fully exploited, which results in the reduction of classification accuracy. In this article, an effective multibranch feature fusion network with self- and cross-guided attention (MB2FscgaNet) is proposed for the joint classification of LiDAR and HSI. The main concern of this article is how to accurately estimate more effective spectral–spatial-elevation features and yield more effective transfer in the network. Specifically, MB2FscgaNet adopts a multibranch feature fusion architecture to fully exploit the hierarchical features from LiDAR and HSI level by level. At each level of the network, a self- and cross-guided attention (SCGA) is developed to assign a higher weight to interesting areas and channels of LiDAR and HSI feature maps to obtain refined spectral–spatial-elevation features and provide complementary information cross-guidance between LiDAR and HS. We further designed a spectral supplement module (SeSuM) to improve the discriminative ability of the center pixel. Comparative classification results and ablation studies demonstrate that the proposed MB2FscgaNet achieves competitive performance against state-of-the-art methods.
The climatology of earth's Na density over Fort Collins, CO (41°N, 105°W) based on nocturnal Na lidar observations between 1990 and 1999 was reported by She et al. (2000, ...https://doi.org/10.1029/2000gl003825). Based on a continued 28‐year data set between 1990 and 2017 with the latter part observed over Logan, UT (42N, 112W), we update the seasonal variations between 80 and 110 km. This data set is also used to deduce long‐term responses of Na density (profile) between 75 and 110 km, showing a positive linear trend between 75 and 93 km (with maximum ∼2.87 × 108 m−3/decade at 87 km); it turns negative before approaching zero at 110 km (with minimum ∼−2.96 × 107 m−3/decade at 100 km). The associated solar response is also positive for the altitude range in question (with maximum ∼5.20 × 106 m−3/SFU at 91 km). We also derived the 28‐year mean Na layer column abundance, centroid altitude, and root mean square width to be 3.92 ± 2.14 1013 m−2, 91.3 ± 1.0 km, and 4.62 ± 0.56 km, respectively, and deduced long‐term trend and solar cycle responses of column abundance and centroid altitude, respectively to be 7.81 ± 1.63%/decade and 16.9 ± 2.8%/100SFU, and −355 ± 35 m/decade and −1.94 ± 0.69 m/SFU. We explained conceptually how positive long‐term responses in Na density led to positive responses in column abundance and negative responses in centroid altitude.
Key Points
Based on 28 years (1340 nights) lidar observation at midlatitude, we report monthly mean Na density profiles between 80 and 110 km
This data set showed a positive linear trend between 75 and 93 km with a maximum at 87 km and positive solar response with a maximum at 91 km
The deduced long‐term solar response of column abundance and centroid linear trend are 16.9 ± 2.8%/100SFU and −355 ± 35 m/decade
Abstract
The dynamics of the dune toe along the urbanised 65 km macro‐tidal coast of Belgium has been examined based on (bi‐)annual cross‐shore profiles derived from airborne LiDAR surveys conducted ...between 2000 and 2019. Results indicate that the average dune toe level is located at +5.9 m Tweede Algemene Waterpassing (TAW, Belgian Ordnance Datum), which is 1 m lower than the conventional dune toe level. However, this level is not static but rather increasing over time with an average rate of 2.3 cm/year, making it comparable with coastal areas along western Europe. Both landward retreat and seaward progradation of the dune toes are observed, with rates up to 2 m/year. The analysis revealed that dune growth is primarily facilitated by the development of incipient or embryonic dunes, new foredunes and modifications to the stoss slope of the original dune profile. Half of the sea‐fronting dunes were observed to be developed by brushwood fences, which had a positive effect on dune growth. At some locations, incipient dune development and shoreline progradation were observed seaward of old foredunes. Other dune regions were characterised by natural dune blowouts and management activities for recreational purposes. Dune toe reinforcements were implemented in the west to stabilise the dune toe and prevent erosion by waves. However, it was noted that if the adjacent beach accreted because of natural growth or periodic nourishment, incipient dunes could form in front of the reinforcement.
Interferometric Synthetic Aperture Radar (InSAR) and lidar are increasingly used active remote sensing techniques for forest structure observation. The TanDEM-X (TDX) InSAR mission of German ...Aerospace Center (DLR) and the upcoming Global Ecosystem Dynamics Investigation (GEDI) of National Aeronautics and Space Administration (NASA) together may provide more accurate estimates of global forest structure and biomass via their synergic use. In this paper, we explored the efficacy of simulated GEDI data in improving height estimates from TDX InSAR data. Our study sites span three major forest types: a temperate forest, a mountainous conifer forest, and a tropical rainforest. The GEDI lidar coverage was simulated for the full nominal two-year mission duration, under both cloud-free and 50%-cloud conditions. We then used these GEDI data to parameterize the Random Volume over Ground (RVoG) model driven by TDX imagery. In particular, we explored the following three strategies for forest structure estimation: 1) TDX data alone; 2) TDX + GEDI-derived digital terrain model (DTM); and 3) TDX + GEDI DTM + GEDI canopy height. We then validated the retrieved forest heights against wall-to-wall airborne lidar measurements. We found relatively large biases at 90 m spatial resolution, from 4.2–11.9 m, and root mean square errors (RMSEs), from 7.9–12.7 m when using TDX data alone under constrained RVoG assumptions of a fixed extinction coefficient (σ) and a zero ground-to-volume amplitude ratio (μ = 0). Results improved significantly with the aid of a DTM derived from GEDI data which enabled estimation of spatially-varying σ values (vs. fixed extinction) under a μ = 0 assumption, with biases reduced to 1.7–4.2 m and RMSEs to 4.9–8.6 m across cloudy and cloud-free cases. The best agreement was achieved in the third strategy by also incorporating information of GEDI-derived canopy height to further enhance the RVoG parameters. The improved model, when still assuming μ = 0, reduced biases to less than or close to 1 m and further reduced RMSEs to 4.0–6.7 m. Finally, we used GEDI data to estimate spatially-varying μ in the RVoG model. We found biases of between −0.7–0.9 m and RMSEs in the range from 2.6–7.1 m over the three sites. Our results suggest that use of GEDI data improves height inversion from TDX, providing heights at more accuracy than can be achieved by TDX alone, and enabling wall-to-wall height estimation at much finer spatial resolution than can be achieved by GEDI alone.
•TanDEM-X InSAR and GEDI lidar data are fused to provide improved forest height.•GEDI data are used to constrain the RVoG model parameters for TanDEM-X data.•Both GEDI elevation and canopy height data are of great use to improve the RVoG.•The fusion approach is promising to provide contiguous forest height maps globally.
Nitrogen (N) is important for the growth of crops. Estimating leaf nitrogen concentration (LNC) accurately and nondestructively is important for precision agriculture, reduces environmental ...pollution, and helps model global carbon and N cycles. Leaf reflectance, especially in the visible and near-infrared regions, has been identified as a useful indicator of LNC. Except reflectance passively acquired by spectrometers, the newly developed multispectral LiDAR and hyperspectral LiDAR provide possibilities for measuring leaf spectra actively. The regression relationship between leaf reflectance spectra and rice (Oryza sativa) LNC relies greatly on the algorithm adopted. It would be preferable to find one algorithm that performs well with respect to passive and active leaf spectra. Thus, this study assesses the influence of six popular linear and nonlinear methods on rice LNC retrieval, namely, partial least-square regression, least squares boosting, bagging, random forest, back-propagation neural network (BPNN), and support vector regression of different types/kernels/parameter values. The R2, root mean square error and relative error in rice LNC estimation using these different methods were compared through the passive and active spectral measurements of rice leaves of different varieties at different locations and time (Yongyou 4949, Suizhou, 2014, Yangliangyou 6, Wuhan, 2015). Results demonstrate that BPNN provided generally satisfactory performance in estimating rice LNC using the three kinds of passive and active reflectance spectra.
This paper presents a short history of the appraisal of laser scanner technologies in geosciences used for imaging relief by high-resolution digital elevation models (HRDEMs) or 3D models. A general ...overview of light detection and ranging (LIDAR) techniques applied to landslides is given, followed by a review of different applications of LIDAR for landslide, rockfall and debris-flow. These applications are classified as: (1) Detection and characterization of mass movements; (2) Hazard assessment and susceptibility mapping; (3) Modelling; (4) Monitoring. This review emphasizes how LIDAR-derived HRDEMs can be used to investigate any type of landslides. It is clear that such HRDEMs are not yet a common tool for landslides investigations, but this technique has opened new domains of applications that still have to be developed.
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