Lidar Data Research Articles

Examining the Impact of Topography and Vegetation on Existing Forest Canopy Height Products from ICESat-2 ATLAS/GEDI Data

Forest canopy height (FCH) is an important variable for estimating forest biomass and ecosystem carbon sequestration. Spaceborne LiDAR data have been used to create wall-to-wall FCH maps, such as the forest tree height map of China (FCHChina), Global Forest Canopy Height 2020 (GFCH2020), and Global Forest Canopy Height 2019 (GFCH2019). However, these products lack comprehensive assessment. This study used airborne LiDAR data from various topographies (e.g., plain, hill, and mountain) to assess the impacts of different topographical and vegetation characteristics on spaceborne LiDAR-derived FCH products. The results show that GEDI–FCH demonstrates better accuracy in plain and hill regions, while ICESat-2 ATLAS–FCH shows superior accuracy in the mountainous region. The difficulty in accurately capturing photons from sparse tree canopies by ATLAS and the geolocation errors of GEDI has led to partial underestimations of FCH products in plain areas. Spaceborne LiDAR FCH retrievals are more accurate in hilly regions, with a root mean square error (RMSE) of 4.99 m for ATLAS and 3.85 m for GEDI. GEDI–FCH is significantly affected by slope in mountainous regions, with an RMSE of 13.26 m. For wall-to-wall FCH products, the availability of FCH data is limited in plain areas. Optimal accuracy is achieved in hilly regions by FCHChina, GFCH2020, and GFCH2019, with RMSEs of 5.52 m, 5.07 m, and 4.85 m, respectively. In mountainous regions, the accuracy of wall-to-wall FCH products is influenced by factors such as tree canopy coverage, forest cover types, and slope. However, some of these errors may stem from directly using current ATL08 and GEDI L2A FCH products for mountainous FCH estimation. Introducing accurate digital elevation model (DEM) data can improve FCH retrieval from spaceborne LiDAR to some extent. This research improves our understanding of the existing FCH products and provides valuable insights into methods for more effectively extracting accurate FCH from spaceborne LiDAR data. Further research should focus on developing suitable approaches to enhance the FCH retrieval accuracy from spaceborne LiDAR data and integrating multi-source data and modeling algorithms to produce accurate wall-to-wall FCH distribution in a large area.

AIRBORNE LASER SCANNER AS A DATA SOURCE FOR BUILDING SELECTED ELEMENTS OF AN INTELLIGENT DATABASE FOR TRANSPORTATION

In this study, the main objective was to detect the road network and key road infrastructure elements based on airborne laser scanning data. The study included identification of the road network and determination of its axes using three independent methods, as well as detection of horizontal signs such as pedestrian crossings. The analysis process was based mainly on digital image processing methods, based solely on lidar data, without using information from other sources. The results of the analysis showed that the use of lidar data provides a fast and effective method for continuously updating information on road infrastructure and expanding the transportation database. This potentially opens the door to effectively updating relevant data in the area of transportation infrastructure.

A machine learning paradigm for necessary observations to reduce uncertainties in aerosol climate forcing.

Uncertainties in estimates of climate cooling by anthropogenic aerosols have not decreased significantly in the last two decades, partly because observational constraints on crucial aerosol properties simulated in Earth System Models are insufficient. To help address this insufficiency in aerosol observations, we describe a paradigm for deriving higher-level aerosol properties with machine learning algorithms that use only lidar observations and reanalysis data as predictors. Our paradigm employs high-accuracy suborbital lidar and collocated in situ measurements to train and test two fully-connected neural network algorithms. We use two lidar data sets as input to our machine learning algorithms. The first data set consists of suborbital lidar observations not previously used in the training of the machine learning algorithms. The second data set consists of simulated UV-only observations to preview the algorithms' predictive capabilities in anticipation of data from the ATmospheric LIDar system on the EarthCARE satellite, which was launched in May 2024. Here we show that our algorithms predict two crucial aerosol properties, aerosol light absorption and cloud condensation nuclei concentrations with unprecedented accuracy, yielding mean relative errors of 21% and 13%, respectively, when suborbital lidar data are used as predictors. These errors represent significant improvements over conventional aerosol retrievals. Applied to future satellite missions, the paradigm presented here has great potential for constraining Earth System Models and reducing uncertainties in their estimates of aerosol climate forcing and future global warming.

Optimal representation of tree foliage for local urban climate modeling

Trees impact the local urban climate, notably at street level by intercepting solar radiation and providing shading. Evapotranspiration in foliage may reduce the air temperature although it may increase relative humidity and leaf drag may reduce wind speed, affecting thermal comfort. To document and quantify this impact, microclimate modeling with Computational Fluid Dynamics (CFD) simulations requires explicit information of the urban configuration, including trees. However, trees are complex individuals with a variety of shapes and a variety of foliage distribution. This study aims to investigate the sensibility to the tree modeling of the urban climate simulations. Starting with terrestrial LiDAR data from trees of different species, ages, and forms, we propose a systematic evaluation of the optimal representation of arboreal configurations in terms of local urban comfort. One way to represent the foliage of trees accurately is to apply Delaunay triangulation on the LiDAR data, which yields a convex envelope model. The resulting foliage shape is very close to the actual tree, but includes a high number of facets leading to complex objects to model numerically. Comparing four species and three maturity level of trees with this method, the paper shows that the size of the zone shadowed by a tree is the parameter with the largest impact on thermal comfort, as the ability of trees to absorb solar radiation is the main asset to improve thermal comfort. The UTCI could be up to 2.1°C lower for a mature ACPL than for a sapling, mainly because the zone covered by the tree is larger. In addition, polyhedron shape rhombicuboctahedron (RBC) produces accurate shadowed zones. Mostly, in literature, tree canopies are modeled with cubic representations while we see that they overestimate the size of the shadowed zone. To have reliable compromise between accuracy and time for conception and computational time, this paper shows that the RBC is the best alternative to common tree models. Despite requiring a good knowledge of the canopy geometry, RBC provides a strong capacity for accurately modelling complex canopy shapes of most tree species and offers large benefits in reduced complexity. We show that the RBC shape, thanks to its simple but flexible geometry, is an efficient and accurate methodological approach to model trees and allows savings in computational time (up to 15% faster than the convex envelope) and costs; and we expect that this method will improve the modeling of further parametric studies on vegetation impact on thermal urban comfort.

Semantic Mapping of Landscape Morphologies: Tuning ML/DL Classification Approaches for Airborne LiDAR Data

The interest in the enhancement of innovative solutions in the geospatial data classification domain from integrated aerial methods is rapidly growing. The transition from unstructured to structured information is essential to set up and arrange geodatabases and cognitive systems such as digital twins capable of monitoring territorial, urban, and general conditions of natural and/or anthropized space, predicting future developments, and considering risk prevention. This research is based on the study of classification methods and the consequent segmentation of low-altitude airborne LiDAR data in highly forested areas. In particular, the proposed approaches investigate integrating unsupervised classification methods and supervised Neural Network strategies, starting from unstructured point-based data formats. Furthermore, the research adopts Machine Learning classification methods for geo-morphological analyses derived from DTM datasets. This paper also discusses the results from a comparative perspective, suggesting possible generalization capabilities concerning the case study investigated.

CALIPSO-based aerosol extinction profile estimation from MODIS and MERRA-2 data using a hybrid model of Transformer and CNN

Acquiring aerosol vertical distribution information is crucial to accurately quantify the aerosol radiation effect on climate and understand the environmental pollution mechanism of the atmosphere. Passive remote sensing has shown its capability to gain large-scale, high spatiotemporal resolution aerosol vertical information such as aerosol layer height (ALH). However, it is still challenging to extract detailed aerosol vertical distribution information, e.g., aerosol extinction profile (AEP), from passive observations. To fill this gap, this study proposed a hybrid model of Transformer and convolutional neural network (CNN) to estimate the AEP from passive multispectral remote sensing (MODIS) measurements with the aid of three-dimensional reanalysis data (MERRA-2). Specifically, the model is learned to estimate the AEP, which is called AproNet, by using the active space-borne lidar (CALIPSO) data as supervised information. Besides, we design a shape invariant loss (SIL) to better capture the shape characteristics of the AEP and incorporate an auxiliary scene awareness loss (SAL) to enhance the model's generalization capacity and physical reliability outside the CALIPSO orbit. The extensive quantitative experiments show that the AEPs estimated by the proposed model agree well with the CALIPSO measurements with an overall performance of IOA=0.821, R=0.800, MAE= 0.014, and RMSE= 0.041, respectively. Qualitative comparisons also demonstrate the model's reliability in estimating the aerosol three-dimensional spatial distribution. Independent year test and comparisons with ground-based lidar measurements further indicate the robustness of the proposed model despite some degradation in performance. However, the incompleteness and uncertainty of the CALIOP products limited the performance of the proposed model to some extent. In the future, the model needs to be further physically constrained and strengthened with more data sources to improve reliability. In general, this study paves the way for acquiring aerosol extinction profiles with high spatiotemporal resolution over a large geographical space.

Seasonal snow–atmosphere modeling: let's do it

Abstract. Mountain snowpack forecasting relies on accurate mass and energy input information in relation to the snowpack. For this reason, coupled snow–atmosphere models, which downscale input fields to the snow model using atmospheric physics, have been developed. These coupled models are often limited in the spatial and temporal extents of their use by computational constraints. In addressing this challenge, we introduce HICARsnow, an intermediate-complexity coupled snow–atmosphere model. HICARsnow couples two physics-based models of intermediate complexity to enable basin-scale snow and atmospheric modeling at seasonal timescales. To showcase the efficacy and capability of HICARsnow, we present results from its application to a high-elevation basin in the Swiss Alps. The simulated snow depth is compared throughout the snow season to aerial lidar data. The model shows reasonable agreement with observations from peak accumulation through late-season melt-out, representing areas of high snow accumulation due to redistribution processes, as well as melt patterns caused by interactions between radiation and topography. HICARsnow is also found to resolve preferential deposition, with model outputs suggesting that parameterizations of the process using surface wind fields may only be inappropriate under certain atmospheric conditions. The two-way coupled model also improves surface air temperatures over late-season snow, demonstrating added value for the atmospheric model as well. Differences between observations and model outputs during the accumulation season indicate a poor representation of redistribution processes away from exposed ridges and steep terrain and a low bias in albedo at high elevations during the ablation season. Overall, HICARsnow shows great promise for applications in operational snow forecasting and in studying the representation of snow accumulation and ablation processes.

Forest Canopy Height Estimation Combining Dual-Polarization PolSAR and Spaceborne LiDAR Data

Forest canopy height data are fundamental parameters of forest structure and are critical for understanding terrestrial carbon stock, global carbon cycle dynamics and forest productivity. To address the limitations of retrieving forest canopy height using conventional PolInSAR-based methods, we proposed a method to estimate forest height by combining single-temporal polarimetric synthetic aperture radar (PolSAR) images with sparse spaceborne LiDAR (forest height) measurements. The core idea of our method is that volume scattering energy variations which are linked to forest canopy height occur during radar acquisition. Specifically, our methodology begins by employing a semi-empirical inversion model directly derived from the random volume over ground (RVoG) formulation to establish the relationship between forest canopy height, volume scattering energy and wave extinction. Subsequently, PolSAR decomposition techniques are used to extract canopy volume scattering energy. Additionally, machine learning is employed to generate a spatially continuous extinction coefficient product, utilizing sparse LiDAR samples for assistance. Finally, with the derived inversion model and the resulting model parameters (i.e., volume scattering power and extinction coefficient), forest canopy height can be estimated. The performance of the proposed forest height inversion method is illustrated with L-band NASA/JPL UAVSAR from AfriSAR data conducted over the Gabon Lope National Park and airborne LiDAR data. Compared to high-accuracy airborne LiDAR data, the obtained forest canopy height from the proposed approach exhibited higher accuracy (R2 = 0.92, RMSE = 6.09 m). The results demonstrate the potential and merit of the synergistic combination of PolSAR (volume scattering power) and sparse LiDAR (forest height) measurements for forest height estimation. Additionally, our approach achieves good performance in forest height estimation, with accuracy comparable to that of the multi-baseline PolInSAR-based inversion method (RMSE = 5.80 m), surpassing traditional PolSAR-based methods with an accuracy of 10.86 m. Given the simplicity and efficiency of the proposed method, it has the potential for large-scale forest height estimation applications when only single-temporal dual-polarization acquisitions are available.

Early spectral dynamics are indicative of distinct growth patterns in post‐wildfire forests

AbstractWestern North America has seen a recent dramatic increase in large and often high‐severity wildfires. After forest fire, understanding patterns of structural recovery is important, as recovery patterns impact critical ecosystem services. Continuous forest monitoring provided by satellite observations is particularly beneficial to capture the pivotal post‐fire period when forest recovery begins. However, it is challenging to optimize optical satellite imagery to both interpolate current and extrapolate future forest structure and composition. We identified a need to understand how early spectral dynamics (5 years post‐fire) inform patterns of structural recovery after fire disturbance. To create these structural patterns, we collected metrics of forest structure using high‐density Remotely Piloted Aircraft (RPAS) lidar (light detection and ranging). We employed a space‐for‐time substitution in the highly fire‐disturbed forests of interior British Columbia. In this region, we collected RPAS lidar and corresponding field plot data 5‐, 8‐, 11‐,12‐, and 16‐years postfire to predict structural attributes relevant to management, including the percent bare ground, the proportion of coniferous trees, stem density, and basal area. We compared forest structural attributes with unique early spectral responses, or trajectories, derived from Landsat time series data 5 years after fire. A total of eight unique spectral recovery trajectories were identified from spectral responses of seven vegetation indices (NBR, NDMI, NDVI, TCA, TCB, TCG, and TCW) that described five distinct patterns of structural recovery captured with RPAS lidar. Two structural patterns covered more than 80% of the study area. Both patterns had strong coniferous regrowth, but one had a higher basal area with more bare ground and the other pattern had a high stem density, but a low basal area and a higher deciduous proportion. Our approach highlights the ability to use early spectral responses to capture unique spectral trajectories and their associated distinct structural recovery patterns.

Determination of Microtopography of Low-Relief Tidal Freshwater Forested Wetlands Using LiDAR

The microtopography of tidal freshwater forested wetlands (TFFWs) impacts biogeochemical processes affecting the carbon and nitrogen dynamics, ecological parameters, and habitat diversity. However, it is challenging to quantify low-relief microtopographic features that might only vary by a few tens of centimeters. We assess the high-resolution fine-scale microtopographic features of a TFFW with terrestrial LiDAR and aerial LiDAR to test a method appropriate to quantify microtopography in low-relief forested wetlands. Our method uses a combination of water-level and elevation thresholding (WALET) to delineate hollows in terrestrial and aerial LiDAR data. Close-range remote sensing technologies can be used for microtopography in forested regions. However, the aerial and terrestrial LiDAR technologies have not been used to analyze or compare microtopographic features in TFFW ecosystems. Therefore, the objectives of this study were (1) to characterize and assess the microtopography of low-relief tidal freshwater forested wetlands and (2) to identify optimal elevation thresholds for widely available aerial LiDAR data to characterize low-relief microtopography. Our results suggest that the WALET method can correctly characterize the microtopography in this area of low-relief topography. The microtopography characterization method described here provides a basis for advanced applications and scaling mechanistic models.

Denoising of Photon-Counting LiDAR Bathymetry Based on Adaptive Variable OPTICS Model and Its Accuracy Assessment

Spaceborne photon-counting LiDAR holds significant potential for shallow-water bathymetry. However, the received photon data often contain substantial noise, complicating the extraction of elevation information. Currently, a denoising algorithm named ordering points to identify the clustering structure (OPTICS) draws people’s attention because of its strong performance under high background noise. However, this algorithm’s fixed input variables can lead to inaccurate photon distribution parameters in areas near the water bottom, which results in inadequate denoising in these areas, affecting bathymetric accuracy. To address this issue, an Adaptive Variable OPTICS (AV-OPTICS) model is proposed in this paper. Unlike the traditional OPTICS model with fixed input variables, the proposed model dynamically adjusts input variables based on point cloud distribution. This adjustment ensures accurate measurement of photon distribution parameters near the water bottom, thereby enhancing denoising effects in these areas and improving bathymetric accuracy. The findings indicate that, compared to traditional OPTICS methods, AV-OPTICS achieves higher F1-values and lower cohesions, demonstrating better denoising performance near the water bottom. Furthermore, this method achieves an average MAE of 0.28 m and RMSE of 0.31 m, indicating better bathymetric accuracy than traditional OPTICS methods. This study provides a promising solution for shallow-water bathymetry based on photon-counting LiDAR data.

Integrating Active and Passive Remote Sensing Data for Forest Age Estimation in Shangri-La City, China

The accurate mapping of age structure and access to spatially explicit information are essential to optimal planning and policy-making for forest ecosystems, including forest management and sustainable economic development. Specifically, surveying and mapping the age structure of forests is crucial for calculating the carbon sequestration capacity of forest ecosystems. However, spatial heterogeneity and limited accessibility make forest age mapping in mountainous areas challenging. Here, we present a new workflow using ICESat-2 LiDAR data integrated with multisource remote sensing imagery to estimate forest age in Shangri-La, China. Two methods—a climate-driven exponential model and a random forest algorithm—are compared to infer the age structure of the five dominant species in Shangri-La. The climate-driven model, with an R2 of 0.67 and an RMSE of 12.79 years, outperforms the random forest model. The derived wall-to-wall forest age map at 30 m resolution reveals that nearly all forests in Shangri-La are mature or overmature, especially among the high-elevation species Abies fabri (Mast.) Craib and Picea asperata Mast., compared with Pinus yunnanensis Franch., Quercus aquifolioides Rehd. and E.H. Wils. and Pinus densata Mast., where the age structure is more evenly distributed across different elevation ranges. Younger forests are frequently found around human settlements and along the Jinsha River valley, whereas older forests are located in remote and high-elevation areas that are less disturbed. The combined use of active and passive remote sensing data has resulted in substantial improvements in the spatial detail and accuracy of wall-to-wall age mapping, which is expected to be a cost-effective approach for supporting forest management and carbon accounting in this important ecological region. The method developed here can be scaled to other mountain areas both to understand the age patterns and structure of mountain forests and to provide critical information for forestation, reforestation and carbon accounting in surface-to-high mountain areas, which are increasingly crucial for climate mitigation.

A singular, broadly-applicable model for estimating on- and off-path walking travel rates using airborne lidar data

Accurate prediction of walking travel rates is central to wide-ranging applications, including modeling historical travel networks, simulating evacuation from hazards, evaluating military ground troop movements, and assessing risk to wildland firefighters. Most of the existing functions for estimating travel rates have focused on slope as the sole landscape impediment, while some have gone a step further in applying a limited set of multiplicative factors to account for broadly defined surface types (e.g., “on-path” vs. “off-path”). In this study, we introduce the Simulating Travel Rates In Diverse Environments (STRIDE) model, which accurately predicts travel rates using a suite of airborne lidar-derived metrics (slope, vegetation density, and surface roughness) that encompass a continuous spectrum of landscape structure. STRIDE enables the accurate prediction of both on- and off-path travel rates using a single function that can be applied across wide-ranging environmental settings. The model explained more than 80% of the variance in the mean travel rates from three separate field experiments, with an average predictive error less than 16%. We demonstrate the use of STRIDE to map least-cost paths, highlighting its propensity for selecting logically consistent routes and producing more accurate yet considerably greater total travel time estimates than a slope-only model.

Novel method for inversion of microphysical properties of clouds using Raman lidar data

Aerosol–cloud–precipitation interactions are important in the balance of Earth’s radiation budget. To further explore the relationship between clouds and precipitation, and to improve operational weather modification, it is necessary to study the microphysical parameters of liquid water clouds. Here, an inversion method that uses a back propagation (BP) neural network based on a genetic algorithm (GA), namely a GABP, is proposed to invert cloud microphysical parameters using ground-based dual-field-of-view (FOV) Raman lidar data. To verify the feasibility of the method, long-term continuous observations were conducted in the Liupan Mountains (China). Results revealed that the proposed inversion method using the GABP is feasible for retrieving the liquid water content (LWC) and the cloud droplet effective radius after training a large number of data measured simultaneously by the Raman lidar and a microwave radiometer. When inverting LWC, the root mean square error (RMSE) of the GABP algorithm was found in the range 0–0.005, whereas the RMSE of the BP algorithm fluctuated in the range 0–0.01. It was evident that the GABP algorithm yields better inversion results and finer detail. When maintaining other variables and comparing the inversion results of signals in the inner and outer FOVs, the RMSE of the inner FOV signal was within 0.005 at near-ground heights (i.e., <2 km), whereas the outer FOV signal exceeded 0.005 at certain heights. This study developed a feasible solution for detecting characteristic cloud microphysical parameters using a Raman lidar, which could be used to study aerosol–cloud–precipitation interactions, and thereby have considerable practical importance for improving artificial rainfall operations.

The southeasternmost area of the long barrow cemeteries of the Funnel Beaker culture in the 4th millennium BC. The case of two newly investigated structures in Pawłosiów forest (Subcarpathian loess region, Poland) in the context of the settlement network

Abstract The eastern part of the Subcarpathian loess region (the Rzeszów Foothills and the Lower San Valley) may now be referred to as the southeasternmost zone of the occurrence of the long barrow cemeteries of the Funnel Beaker culture. In recent times, new intriguing mounds have been discovered in Pawłosiów forest through the utilisation of LiDAR data sources. In order to investigate whether these structures are long barrows built by “beakers” communities, we have carried out preliminary geophysic research, geological drillings, and lithological analysis. As a result, we confirmed the anthropogenic origin of both structures. The main feature registered during the fieldwork was the lithological and stratigraphical difference between the mounds and their direct geological context. Moreover, a presence of an archaeological feature was identified within the frontal part of one of the mounds. These facts, together with key information regarding the location of both earthen structures in the field and their spatial relationship to the nearby FBC settlement at site 52 in Pawłosiów, suggest that they may be relics of the FBC long barrows. In this study, we also discussed these mounds in the context of their spatial-chronological position in the settlement network(s). It is plausible that the inhabitants of Pawłosiów 52 settlement were responsible for the construction of both long barrows in the period ca. 3650–3400 BC. Additionally, we do not exclude the possible alternative (non-funeral) functions and meanings of the cemetery.

Optimizing Local Climate Zones through Clustering for Surface Urban Heat Island Analysis in Building Height-Scarce Cities: A Cape Town Case Study

Studying air Urban Heat Islands (AUHI) in African cities is limited by building height data scarcity and sparse air temperature (Tair) networks, leading to classification confusion and gaps in Tair data. Satellite imagery used in surface UHI (SUHI) applications overcomes the gaps which befall AUHI, thus making it the primary focus of UHI studies in areas with limited Tair stations. Consequently, we used Landsat 30 m imagery to analyse SUHI patterns using Land Surface Temperature (LST) data. Local climate zones (LCZ) as a UHI study tool have been documented to not result in distinct thermal environments at the surface level per LCZ class. The goal in this study was thus to explore relationships between LCZs and LST patterns, aiming to create a building height (BH)-independent LCZ framework capable of creating distinct thermal environments to study SUHI in African cities where LiDAR data are scarce. Random forests (RF) classified LCZ in R, and the Single Channel Algorithm (SCA) extracted LST via the Google Earth Engine. Statistical analyses, including ANOVA and Tukey’s HSD, assessed thermal distinctiveness, using a 95% confidence interval and 1 °C threshold for practical significance. Semi-Automated Agglomerative Clustering (SAAC) and Automated Divisive Clustering (ADC) grouped LCZs into thermally distinct clusters based on physical characteristics and LST data internal patterns. Built LCZs (1–9) had higher mean LSTs; LCZ 8 reached 37.6 °C in Spring, with a smaller interquartile range (IQR) (34–36 °C) and standard deviation (SD) (1.85 °C), compared to natural classes (A–G) with LCZ 11 (A–B) at 14.9 °C/LST, 17–25 °C/IQR, and 4.2 °C SD. Compact LCZs (2, 3) and open LCZs (5, 6), as well as similar LCZs in composition and density, did not show distinct thermal environments even with building height included. The SAAC and ADC clustered the 14 LCZs into six thermally distinct clusters, with the smallest LST difference being 1.19 °C, above the 1 °C threshold. This clustering approach provides an optimal LCZ framework for SUHI studies, transferable to different urban areas without relying on BH, making it more suitable than the full LCZ typology, particularly for the African context. This clustered framework ensures a thermal distinction between clusters large enough to have practical significance, which is more useful in urban planning than statistical significance.

Large-Scale Network-Based Observations of a Saharan Dust Event across the European Continent in Spring 2022

Between 14 March and 21 April 2022, an extensive investigation of an extraordinary Saharan dust intrusion over Europe was performed based on lidar measurements obtained by the European Aerosol Research Lidar Network (EARLINET). The dust episode was divided into two distinct periods, one in March and one in April, characterized by different dust transport paths. The dust aerosol layers were studied over 18 EARLINET stations, examining aerosol characteristics during March and April in four different regions (M-I, M-II, M-III, and M-IV and A-I, A-II, A-III, and A-IV, respectively), focusing on parameters such as aerosol layer thickness, center of mass (CoM), lidar ratio (LR), particle linear depolarization ratio (PLDR), and Ångström exponents (ÅE). In March, regions exhibited varying dust geometrical and optical properties, with mean CoM values ranging from approximately 3.5 to 4.8 km, and mean LR values typically between 36 and 54 sr. PLDR values indicated the presence of both pure and mixed dust aerosols, with values ranging from 0.20 to 0.32 at 355 nm and 0.24 to 0.31 at 532 nm. ÅE values suggested a range of particle sizes, with some regions showing a predominance of coarse particles. Aerosol Optical Depth (AOD) simulations from the NAAPS model indicated significant dust activity across Europe, with AOD values reaching up to 1.60. In April, dust aerosol layers were observed between 3.2 to 5.2 km. Mean LR values typically ranged from 35 to 51 sr at both 355 nm and 532 nm, while PLDR values confirmed the presence of dust aerosols, with mean values between 0.22 and 0.31 at 355 nm and 0.25 to 0.31 at 532 nm. The ÅE values suggested a mixture of particle sizes. The AOD values in April were generally lower, not exceeding 0.8, indicating a less intense dust presence compared to March. The findings highlight spatial and temporal variations in aerosol characteristics across the regions, during the distinctive periods. From 15 to 16 March 2022, Saharan dust significantly reduced UV-B radiation by approximately 14% over the ATZ station (Athens, GR). Backward air mass trajectories showed that the dust originated from the Western and Central Sahara when, during this specific case, the air mass trajectories passed over GRA (Granada, ES) and PAY (Payerne, CH) before reaching ATZ, maintaining high relative humidity and almost stable aerosol properties throughout its transport. Lidar data revealed elevated aerosol backscatter (baer) and PLDR values, combined with low LR and ÅE values, indicative of pure dust aerosols.

Pavement damage characteristics in the permafrost regions based on UAV images and airborne LiDAR data

The rapid degradation of “Xing'an-Baikal permafrost” in Northeast China has led to various road engineering problems. Efficient inspection and control of pavement quality are critical for maintaining the structural integrity of roads and driving safety in cold regions. Taking the Jagdaqi-Walagan section (JWS) of the Jagdaqi-Mo'he Highway as the object, based on field investigation, unmanned aerial vehicle images and airborne LiDAR data, combined with geographical information system, this study analyzed the pavement damage characteristics in mid- to high-latitude permafrost regions, including quantification of damage ratio, extraction of pavement cracks, and evaluation of pavement roughness and driving quality. The results showed that the average pavement damage ratio was 8.80 %, significantly higher in isolated permafrost regions. A higher damage rate in the Jagdaqi-Mo'he direction than the opposite, with a more concentrated cracking distribution. The worst pavement roughness and most severe pavement bumping at repetitive repair locations. This study provides an effective method for investigating pavement damages and analyzing their mechanisms, and explores the application potential of visible light images combined with LiDAR data in frozen soil engineering. The results provide a scientific basis for assessing current highway conditions, enabling scientific maintenance, and evaluating the risk of engineering damages.

Evaluation of GEDI footprint level biomass models in Southern African Savannas using airborne LiDAR and field measurements

Savannas cover more than 20% of the Earth and account for the third largest stock of global aboveground biomass yet estimates of their above ground biomass density (AGBD) are very inaccurate. The Global Ecosystem Dynamic Investigation (GEDI) sensor provides near-global full-waveform LiDAR data with 25 m footprints, from which various structural metrics are derived that are used to predict footprint level AGBD. The current GEDI L4A AGBD product uses a comprehensive Forest Structure and Biomass Database (FSBD) to develop models for specific plant functional types and geographic regions, but southern African savannas have been underrepresented in the reference data. The objectives of this study were to (i) validate GEDI L4A AGBD in South African savannas using field measurements and ALS datasets and (ii) develop and evaluate local GEDI footprint-level AGBD estimates from multiple L2A and L2B metrics. The local GEDI AGBD models outperformed GEDI L4A AGBD (R2 = 0.42, RMSE = 12 Mg/ha, %RMSE = 79.5%) with higher R2 and smaller error measures. The local GEDI AGBD using a random forest model (RF) had the highest R2 of 0.71 and lowest %RMSE of 53.3%, while the generalized linear model (GLM) results provided the lowest Relative Mean Systematic Deviation (RMSD) of 9.2%, which was half that of RF model. L4A significantly underestimated AGBD with an RMSD up to −37%. This highlights the importance and benefits of local calibration of biomass models to unlock the full potential of GEDI metrics for estimating AGBD. The field and ALS data have subsequently been contributed to the GEDI FSBD and should be used in calibration of future versions of GEDI L4A AGBD product. This research paves the way for the integration of the local GEDI AGBD estimates with other sensors, notable the eminent NISAR mission, to derive regional to global gridded AGBD products that will enable the monitoring of savanna carbon stocks.

Point Cloud Densification Algorithm for Multiple Cameras and Lidars Data Fusion.

Fusing data from many sources helps to achieve improved analysis and results. In this work, we present a new algorithm to fuse data from multiple cameras with data from multiple lidars. This algorithm was developed to increase the sensitivity and specificity of autonomous vehicle perception systems, where the most accurate sensors measuring the vehicle's surroundings are cameras and lidar devices. Perception systems based on data from one type of sensor do not use complete information and have lower quality. The camera provides two-dimensional images; lidar produces three-dimensional point clouds. We developed a method for matching pixels on a pair of stereoscopic images using dynamic programming inspired by an algorithm to match sequences of amino acids used in bioinformatics. We improve the quality of the basic algorithm using additional data from edge detectors. Furthermore, we also improve the algorithm performance by reducing the size of matched pixels determined by available car speeds. We perform point cloud densification in the final step of our method, fusing lidar output data with stereo vision output. We implemented our algorithm in C++ with Python API, and we provided the open-source library named Stereo PCD. This library very efficiently fuses data from multiple cameras and multiple lidars. In the article, we present the results of our approach to benchmark databases in terms of quality and performance. We compare our algorithm with other popular methods.