LiDAR Surveys Research Articles

Quantifying landscape change following catastrophic dam failures in Edenville and Sanford, Michigan, USA

AbstractDam failures due to changing hydroclimate and ageing infrastructure pose a significant threat to downstream river systems and communities. The detailed geomorphic effects of catastrophic dam failures are not well known because of a lack of high‐resolution topographic data before and after failures. On 19 May 2020, the 17‐m‐tall Edenville and 11‐m‐tall Sanford dams near Midland, Michigan, USA, failed as a result of significant rainfall over the preceding 2 days. We analysed the geomorphic impacts of these failures using a pre‐failure airborne lidar dataset and three uncrewed aerial vehicle (UAV)‐based lidar surveys collected 2 weeks, 3 months and 11 months after failure. Our survey following the dam failure revealed 47 100 ± 11 900 m3 of net floodplain erosion and 17 300 ± 4200 m3 of net deposition downstream of the Edenville and Sanford dam breaches, respectively. Over the year following failure, most geomorphic change was confined to new knickpoints migrating slowly through underlying glacial till substrate, with little change to riverbanks or surrounding floodplains. A lack of impounded reservoir sediment and antecedent downstream topography, including valley width and the location of the breaches relative to the river below the dams, contributed to relatively modest geomorphic changes despite the magnitude of water released. We provide insight into how landscapes are shaped by catastrophic floods, which are likely to become more common with ageing dams and a changing hydroclimate.

Exploring the Baltinglass cursus complex: routes for the dead

Clusters of Neolithic cursus monuments are attested in several parts of Britain but have so far not been recorded in Ireland, where only isolated or pairs of monuments are known. A recent lidar survey of the Baltinglass landscape of County Wicklow, Ireland, has now identified a cluster of up to five cursus monuments. Here, the author explores this group of monuments and their significance within the wider setting of Neolithic Ireland and Britain. Their unique morphology, location and orientation offer insights into the ritual and ceremonial aspects of the farming communities that inhabited the Baltinglass landscape and hint at the variability in the form and possible functions of these monuments for early farming communities.

Remote Quantification of Soil Organic Carbon: Role of Topography in the Intra-Field Distribution

Soil organic carbon (SOC) measurements are an indicator of soil health and an important parameter for the study of land-atmosphere carbon fluxes. Field sampling provides precise measurements at the sample location but entails high costs and cannot provide detailed maps unless the sampling density is very high. Remote sensing offers the possibility to quantify SOC over large areas in a cost-effective way. As a result, numerous studies have sought to quantify SOC using Earth observation data with a focus on inter-field or regional distributions. This study took a different angle and aimed to map the spatial distribution of SOC at the intra-field scale, since this distribution provides important insights into the biophysiochemical processes involved in the retention of SOC. Instead of solely using spectral measurements to quantify SOC, topographic and spectral features act as predictor variables. The necessary data on study fields in South-East England was acquired through a detailed SOC sampling campaign, including a LiDAR survey flight. Multi-spectral Sentinel-2 data of the study fields were acquired for the exact day of the sampling campaign, and for an interval of 18 months before and after this date. Random Forest (RF) and Support Vector Regression (SVR) models were trained and tested on the spectral and topographical data of the fields to predict the observed SOC values. Five different sets of model predictors were assessed, by using independently and in combination, single and multidate spectral data, and topographical features for the SOC sampling points. Both, RF and SVR models performed best when trained on multi-temporal Sentinel-2 data together with topographic features, achieving validation root-mean-square errors (RMSEs) of 0.29% and 0.23% SOC, respectively. These RMSEs are competitive when compared with those found in the literature for similar models. The topographic wetness index (TWI) exhibited the highest permutation importance for virtually all models. Given that farming practices within each field are the same, this result suggests an important role of soil moisture in SOC retention. Contrary to findings in dryer climates or in studies encompassing larger areas, TWI was negatively related to SOC levels in the study fields, suggesting a different role of soil wetness in the SOC storage in climates characterized by excess rainfall and poorly drained soils.

How does ski infrastructure change soil erosion processes on hillslope?

AbstractSki tourism's popularity is driving a rise in the number of ski resorts. This study aims to present the impact of ski infrastructure on soil erosion processes in the example from a small catchment in the Gubałowskie Foothills in southern Poland, where landscape changes before (since 1879) and after the construction of the ski station (2007) are presented. The analyses of changes in flow accumulation, slope morphometry, and drainage ditches were conducted in the test area. Quantitative analyses were performed using repeated DEMs derived from aerial LiDAR survey and detailed geodetic measurements, complemented by geomorphological mapping done in the field. The study has revealed that the ski infrastructure has not only directly transformed the hillslope by flattening and constructing escarpments (up to 3 m high) but has also created alternating patterns of erosion and accumulation. In the test area, the drainage ditch was poorly designed. It was filled with materials (0.1–0.5 m), and two new outlets formed. The escarpment of the analyzed ski run has been diminished by 0.5 m. An alluvial fan (0.1–0.22 m thick) has developed on the flattened surface below the escarpment with drainage ditch outlets. This fan is eroded by subsurface flow that creates a piping system. The gully below the alluvial fan has retreated upslope, accelerated by subsurface erosion. This study enables the presentation of hillslope adjustments and processes in response to the new conditions caused by ski infrastructure. Such results may support more effective land management in regions changed by ski infrastructure.

Low-Density Urbanisation: Prestate Settlement Growth in a Pacific Society

The recognition of low-density urbanisation has been important in documenting how diverse human settlements generated enduring social and economic change. In tropical regions, the key challenges to studying low-density urbanisation have been the difficulty in acquiring past built environment data and integrating the frameworks that illuminate the social behaviours intrinsic to urbanisation. The introduction of lidar mapping and urban science methods has proven revolutionary in our understanding of low-density urbanisation as demonstrated by emerging research on settlements and states in Mesoamerica and Southeast Asia. These studies draw on urban theory to highlight patterns in the built environment associated with profound societal changes including the rise of social institutions, agglomeration effects, and ongoing settlement growth. Here, we present an approach that combines lidar survey and archaeological fieldwork with recent developments in urban science to understand the built environment of Tongatapu; the location of an archaic state whose influence spread across the southwest Pacific Ocean between the thirteenth and nineteenth centuries a.d. Quantitative results show—for the first time—that settlements on a Pacific island were urbanised in a distinct low-density form and that the processes of urbanisation began prior to state development. This study highlights the potential contribution of Pacific landscapes to urban science and the low-density settlement phenomena given the presence of large populations, hierarchical societies, and vast distributions of archaeological built remains on many island groups.

Recent changes in riparian and floodplain vegetation in England and Wales and its geomorphic implications

AbstractRecent river studies in the United Kingdom have observed an increase in riparian vegetation and its potential impact on river channel change and sedimentation. Here, we quantify changes in floodplain and riparian vegetation during the first two decades of the 21st century along reaches of eight gravel‐bed rivers in England and Wales that exhibit varied active and stable, single and multi‐thread planforms. The analysis employs information drawn from open‐access sources including national LiDAR surveys and the photographic analysis of repeat aerial and satellite imagery. Most reaches show an increase in woody vegetation across their floodplains reflecting both natural colonisation and deliberate planting, and all but one of the reaches show an increase in woody riparian vegetation cover along their riverbanks. Of the six reaches that have exposed riverine sediments, five show a reduction in exposed sediment area as a result of vegetation encroachment. Contrasts in the rate, extent and location of riparian change were associated with the stability of the channel planform, with differences seen between stable and more active reaches. The significance of riparian woodland in promoting riverbank stability is demonstrated. Riparian vegetation development varies between river reaches, largely in response to differing rates of channel movement but also because of multiple and diverse local decisions regarding the management of in‐channel and channel‐adjacent vegetation by landowners rather than a single national policy. We consider the relevance of our findings and the value of remote monitoring for future river management interventions.

Shortening fire return interval predisposes west-central Canadian boreal peatlands to more rapid vegetation growth and transition to forest cover.

Climate change in northern latitudes is increasing the vulnerability of peatlands and the riparian transition zones between peatlands and upland forests (referred to as ecotones) to greater frequency of wildland fires. We examined early post-fire vegetation regeneration following the 2011 Utikuma complex fire (central Alberta, Canada). This study examined 779 peatlands and adjacent ecotones, covering an area of ~182 km2 . Based on the known regional fire history, peatlands that burned in 2011 were stratified into either long return interval (LRI) fire regimes of >80 years (i.e., no recorded prior fire history) or short fire return interval (SRI) of 55 years (i.e., within the boundary of a documented severe fire in 1956). Data from six multitemporal airborne lidar surveys were used to quantify trajectories of vegetation change for 8 years prior to and 8 years following the 2011 fire. To date, no studies have quantified the impacts of post-fire regeneration following short versus long return interval fires across this broad range of peatlands with variable environmental and post-fire successional trajectories. We found that SRI peatlands demonstrated more rapid vascular and shrub growth rates, especially in peatland centers, than LRI peatlands. Bogs and fens burned in 1956, and with little vascular vegetation (classified as "open peatlands") prior to the 2011 fire, experienced the greatest changes. These peatlands tended to transition to vascular/shrub forms following the SRI fire, while open LRI peatlands were not significantly different from pre-fire conditions. The results of this study suggest the emergence of a positive feedback, where areas experiencing SRI fires in southern boreal peatlands are expected to transition to forested vegetation forms. Along fen edges and within bog centers, SRI fires are expected to reduce local peatland groundwater moisture-holding capacity and promote favorable conditions for increased fire frequency and severity in the future.

Lidar Research during the First Stage of the 89th Cruise of the R/V “Academic Mstislav Keldysh”

A lidar survey of the western part of the Kara Sea was carried out in September 2022. The shipborne radiometric (profiling) lidar PLD-1 was used. The lidar optical unit was located on the 8th deck of the R/V ‘Akademik Mstislav Keldysh’ at an altitude of 15 m above the water surface. Lidar sounding was carried out at stations and underway. The vessel route passed through water areas characterized by a wide range of changes in hydrooptical characteristics. Lidar measurements were accompanied by synchronized measurements of hydrooptical and hydrological characteristics. These measurements were carried out using submersible instruments at stations and using a flow-through measuring complex along the ship's route. The hydrooptical characteristics vertical distribution uniformity in the upper ten-meter layer was controlled remotely using underway lidar data.Good agreement between the spatial distributions of the lidar echo signals parameters, of the hydrooptical and of the hydrological characteristics (coincidence of the locations of various distribution features, local maxima, minima and frontal zones) was demonstrated. A large volume of measurement data has been obtained, which allows for further statistical processing in order to find relationships between the parameters of lidar echo signals and hydrooptical characteristics recorded by contact methods.

МЕТОДИКА УТОЧНЕНИЯ ЦИФРОВЫХ МОДЕЛЕЙ РЕЛЬЕФА ОТКРЫТЫХ ГОРНЫХ ВЫРАБОТОК ПО МАТЕРИАЛАМ ЛАЗЕРНОГО СКАНИРОВАНИЯ И АЭРОФОТОСЪЕМКИ

The article presents the results of research on the refinement of digital terrain models using point clouds obtained during lidar surveys or based on aerial photography from unmanned aircraft. It is established that when modeling various forms only on the basis of a set of key points, ambiguity may arise. It is determined that when using TIN models to clarify the position of the faces, it is advisable to use redundant points of the «ground» class. The variants of the implementation of approaches using the least squares method are shown. Their effectiveness and expediency are revealed on specific examples. The results of the research can be useful in work related to digital terrain modeling, including in relation to open-pit mining.

Mobile Terrestrial LiDAR survey for rockfall risk management along a highway

In the alpine stretch, from Brenner to Bolzano (Northern Italy), the Highway A22 is exposed to rockfalls and protection structures must be progressively extended as new detachments are identified. This paper defines a procedure to manage the rockfall risk through periodic Mobile Terrestrial LiDAR surveys from a vehicle travelling along the highway. This type of survey requires the adoption of specific precautions to work safely without interrupting traffic on the highway, such as escorting the surveyors’ vehicle using specific vehicles for mobile road works. Prior to a field trial, the potential effectiveness of a Mobile Terrestrial LiDAR survey was demonstrated through a GIS-based simulation. The simulation was performed for the most critical cliffs in a GRASS GIS environment with the support of Python scripts and the results were compared with those from a simulation of a static survey. Still working in a GIS environment, eight cliffs were identified as the most susceptible to rockfalls that may interfere with the infrastructures. The survey simulation revealed that a Mobile Terrestrial LiDAR survey has the potential to sense over 85% of the cliff area with a point density greater than 100 pts/m2. Given these promising results, a trial survey was then carried out in April 2023 using the innovative mobile mapping system Trimble MX9. Half a day was sufficient to sense the rock slopes along a 30 km stretch of the motorway with a point density of up to 900 pts/m2 for distances up to 150 m.

Analysis of Depths Derived by Airborne Lidar and Satellite Imaging to Support Bathymetric Mapping Efforts with Varying Environmental Conditions: Lower Laguna Madre, Gulf of Mexico

In 2017, Bureau of Economic Geology (BEG) researchers at the University of Texas at Austin (UT Austin) conducted an airborne lidar survey campaign, collecting topographic and bathymetric data over Lower Laguna Madre, which is a shallow hypersaline lagoon in south Texas. Researchers acquired 60 hours of lidar data, covering an area of 1600 km2 with varying environmental conditions influencing water quality and surface heights. In the southernmost parts of the lagoon, in-situ measurements were collected from a boat to quantify turbidity, water transparency, and depths. Data analysis included processing of Sentinel-2 L1C satellite imagery pixel reflectance to classify locations with intermittent turbidity. Lidar measurements were compared to sonar recordings, and results revealed height differences of 5–25 cm where the lagoon was shallower than 3.35 m. Further, researchers analyzed satellite bathymetry at relatively transparent lagoon locations, and the results produced height agreement within 13 cm. The study concluded that bathymetric efforts with airborne lidar and optical satellite imaging have practical limitations and comparable results in large and dynamic shallow coastal estuaries, where in-situ measurements and tide adjustments are essential for height comparisons.

Accuracy evaluation and effect factor analysis of GEDI aboveground biomass product for temperate forests in the conterminous United States

ABSTRACT The Global Ecosystem Dynamics Investigation (GEDI) is expected to revolutionize the quantification of aboveground carbon at continental scales, through its unprecedented dense vertical observations of vegetation structure. As its primary task, GEDI recently introduced GEDI L4A, the 25 m near-global footprint aboveground biomass density (AGBD) product. As a global mission with significant policy and management applications, it is urgent to conduct a comprehensive evaluation of GEDI L4A and to analyze the factors affecting the product’s performance. In this study, the accuracy of GEDI L4A is assessed using co-registered airborne Lidar surveys collected during 2018 ~ 2019 and corresponding AGBD plots at 19 sites of the National Ecological Observatory Network (NEON). The analysis included 11 forest types and spanned 17 eco-climatic domains across the conterminous United States to ensure the representativeness and comprehensiveness of the evaluation result. The interplay of nine factors affecting GEDI L4A is quantified, including the simulated waveform strategy deviation (SWSD) used in GEDI L4A, canopy characteristics (tree height, crown size, and canopy cover), canopy heterogeneity (crown size standard deviation, tree height standard deviation, and tree density), and other factors (forest type and topographic slope). Results show that compared with NEON observations, GEDI L4A generally underestimates the AGBD (Bias: −31.65 Mg/ha), with a moderate relative error exhibited in 14 of 19 sites (%RMSE ranging from 19% to 50%). For half of the forest types, the threshold of the lowest accuracy requirement of AGBD products set by GCOS was met or was close to being met. Broadleaf forests with high AGBD values had the lowest %RMSE (less than 35%), while coniferous forests with low AGBD values had the highest %RMSE (over 50%). Among the different factors considered, the SWSD contributed the most to GEDI L4A’s accuracy, with a relative importance of 56.63%, and manifested the indirect impacts of canopy heterogeneity and canopy characteristics. The relative importance of canopy heterogeneity (32.40%) was the second highest after SWSD; it was also much higher than that of canopy characteristics (3.99%). These results indicate the limitation of using only relative heights as predictors in GEDI L4A due to limited representation of horizontal structure and vertical tree complexity within a footprint. The findings in this study are a step forward in GEDI L4A’s appropriate application and provide perspectives to aid its improvement.

Estimation of Carbon Stocks of Birch Forests on Abandoned Arable Lands in the Cis-Ural Using Unmanned Aerial Vehicle-Mounted LiDAR Camera

Currently, studies investigating the carbon balance in forest ecosystems are particularly relevant due to the global increase in CO2 content in the atmosphere. Due to natural reforestation over the past 25–30 years, birch (Betula pendula Roth.) forests were extensively grown and established on abandoned agricultural lands in Bashkir Cis-Ural (Republic of Bashkortostan, Russia). The significant positive aspect of reforestation on fallow lands is the carbon sequestration that takes place in the tree phytomass, especially at the growth stage of stand formation. The aim of this article is to test the approach of using a UAV-mounted LiDAR camera to estimate the phytomass and carbon stocks in different-aged birch forests growing on abandoned arable lands in Bashkir Cis-Ural. The methodology was developed using 28 sample plots, where the LiDAR survey was performed using a DJI Matrice 300 RTK UAV. Simultaneously, the stand characteristics and phytomass of stem wood were also estimated, using traditional methods in the field of forest science. The regression equations of phytomass dependence on stand characteristics at different stages of reforestation were constructed using data obtained from LiDAR imagery. It was shown that the above-ground tree biomass could be precisely estimated using the index obtained by multiplying the number of trees and their average height. A comparison of the data obtained using traditional and LiDAR survey methods found that the accuracy of the latter increased in conjunction with stand density. The accuracy of estimation ranged from 0.2 to 6.8% in birch forests aged 20 years and over. To calculate carbon stocks of the above-ground tree stands, the use of regional conversion coefficients is suggested, which could also be applied for the estimation of carbon content in trunk wood and leaves. An equation for the calculation of above-ground biomass carbon stocks of birch forests on abandoned arable lands is proposed.

Anthropogenic soils in a marginal landscape – Fortified manorial sites in the woodland edge zone in western Czechia

This study presents the results of a geochemical and LiDAR survey analysis carried out at the deserted medieval settlement and fortified manorial farm (motte-and-bailey) in Čečín, in Czechia (Western Bohemia, Plzeň-North District). Focusing on the site of the medieval fortification and surrounding areas, different techniques of compositional data analysis have been used in order to detect geochemical signals. The most significant anthropogenic signal was picked up within the area of the fortification itself (i.e. the motte, elements P, Ca, Mn, and Zn); in contrast to this, samples from other areas – part of the bailey/manor, and the nearby fields and forest – did return weaker, or no such signals, as was documented by individual elemental concentrations, and by the results of PCA. The analysis of historical topographical, cartographical and LiDAR data points to continuity in the economic use of the area in medieval and post-medieval times and to the possibly marginal, or secondary role of arable management at this site. One may hypothetically argue that geochemical observations (i.e. the relatively less significant enrichment and concentration of anthropogenic elements at Čečín, compared to other sites) may also substantiate the claim, or interpretation that this landscape, in the vicinity of the manor, was less impacted by farming and fertilization, and its economic functions were other than crop farming (e.g. meadow, pasture, orchard). There have been a few similar sites (i.e. fortified manorial farms) identified in the LiDAR survey of the estate of the Plasy monastery, in the woodland area and in the woodland edge zone, which have been/are currently used as meadows, in the vicinity of hunting lodges. Their historical role – as medieval manorial sites – likely contribute to the fact that they appear now, in geochemical surveys, as ‘fertile’ islands surrounded by temperate forests.

EFFICIENT UAV FLIGHT PLANNING FOR LOD2 CITY MODEL IMPROVEMENT

Abstract. Geometric errors in LoD2 building models can be caused by the modeling algorithm but are often related to the quality of input data. One approach to tackling the modeling errors caused by the quality of input data is to collect additional data with a UAV and remodel the buildings. However, no flight planning approach exists specifically designed for efficient data recollection for model improvement. In this paper, we propose an innovative flight planning approach for this purpose. Contrary to the conventional method that recollects the data covering the entire building roof, our approach only collects the data over the erroneous region and uses it to improve the erroneous model part later. Our algorithm utilizes the existing LiDAR survey data to automatically detect model errors and design the camera networks by considering the roof geometry. We optimize the trajectory that connects the viewpoints with a genetic algorithm and develops an obstacle avoidance function with ray-casting to ensure a collision-free path. The proposed flight plan is implemented in a real-world scene. Our result shows an improved point cloud created through dense image matching with the collected UAV image data. The generated point cloud is successfully used for creating partial building models for improving the original models.

Aeromagnetic surveys for the location of undocumented orphaned wells

The U.S. Department of Energy's National Energy Technology Laboratory (NETL) conducted research to field test the ability of airborne methods to locate existing wells quickly and accurately with minimal landowner impact. Helicopter surveys with two boom-mounted atomic magnetometers were used to locate wells at two oil fields in Wyoming and at four large oil and gas producing areas in Pennsylvania. At the two Wyoming oil fields, the helicopter magnetic survey identified 91%–96% of the wells located by exhaustive ground search. A different validation was used for the thickly forested Pennsylvania flight areas where well picks from the helicopter magnetic survey were used to direct the ground search to potential well locations for verification. The number and location of verified well locations were then compared to well records in the state database. For the oldest wells (circa 1860–1880), the number of wells in the state database exceeded the number of wells identified by the helicopter magnetic survey because many early wells had wood casing (nonmagnetic). For wells drilled between 1880 and 1950, the helicopter surveys found two to five undocumented wells for each well in the state database. The best results were achieved at a gas storage field where wells were drilled after 1950. There, 98% of the wells located by the helicopter magnetic survey were documented. In 2017, NETL used an atomic magnetometer on a small uncrewed aircraft system (sUAS) to refly an area previously flown with a crewed helicopter. The magnetic map from the sUAS survey was equal to that obtained with a crewed helicopter. High-resolution topographic images developed from sUAS LiDAR surveys were found to be helpful in locating very early wells with wood casing. When the wood casing decayed, soil subsided into the well void, leaving a circular depression that is easily recognized in high-resolution LiDAR imagery.

Geomorphology of the upper sector of the Roncovetro active landslide (Emilia-Romagna Region, Italy)

ABSTRACT We present the geomorphological map of the upper sector of the Roncovetro active landslide (Enza Valley, Emilia-Romagna, Italy). The 1:1500 scale map provides an accurate picture of the landslide in October 2014. The map is mainly based on the data collected during an airborne LiDAR survey. The capability of LiDAR to ‘penetrate’ the vegetation cover makes these data the most complete and accurate topographic dataset of this landslide. The map shows that the upper sector of the Roncovetro landslide consists of gravity- and water runoff-related forms. Gravitational features are linked to sliding and flowing movements that characterize the short- and long-term behaviour of the landslide. By comparing the 2014 LiDAR-Digital Elevation Model (DEM) with the 1973 DEM provided by the Emilia-Romagna Region, we calculated that 6.2 ± 0.8 × 105 m3 of material has moved from the top of the Roncovetro landslide in about 40 years.

Statistically derived morphological signatures of large river channels extracted from topo‐bathymetric LiDAR data

AbstractWith the development of LiDAR technology and the availability of topo‐bathymetric data of high quality, new methods are emerging to describe and understand more accurately fluvial geomorphology. We explore the capacities of probability density functions (PDFs) of detrended dimensionless elevations extracted from digital elevation models (DEMs) to document channels morphology and configuration. These DEMs were obtained from a topo‐bathymetric LiDAR survey of 450 km performed on the middle and lower reaches of the largest river of France: the Loire River. The objective is to propose a thorough and complete method for PDF analysis in order to assess the vertical structuration of river reaches presenting various fluvial patterns. The analysis was conducted on the PDFs of active channel belt of four sites selected for the diversity of their morphological configurations (anabranching, braided, channelized and meandering). Results showed that PDFs appear to be specific of a reach and that they are useful for describing its vertical structure. The simplification with Gaussian mixture model (GMM) is effective in slicing PDF and the resulting Gaussians components are related to morphological units (MUs). The distance between Gaussian curves obtained using GMMs can be considered as an indicator of topo‐bathymetric connectivity between MUs (e.g., main and side channels) on sites presenting multichannel or channelized channel configurations. The study showed the high potential of PDF and GMM analysis in the field of fluvial geomorphology and suggests interesting developments for river management and restoration.

High-Resolution Real-Time Coastline Detection Using GNSS RTK, Optical, and Thermal SfM Photogrammetric Data in the Po River Delta, Italy

High-resolution coastline detection and monitoring are challenging on a global scale, especially in flat areas where natural events, sea level rise, and anthropic activities constantly modify the coastal environment. While the coastline related to the 0-level contour line can be extracted from accurate Digital Terrain Models (DTMs), the detection of the real-time, instantaneous coastline, especially at low tide, is a challenge that warrants further study and evaluation. In order to investigate an efficient combination of methods that allows to contribute to the knowledge in this field, this work uses topographic total station measurements, Global Navigation Satellite System Real-Time Kinematic (GNSS RTK) technique, and the Structure from Motion (SfM) approach (using a low-cost drone equipped with optical and thermal cameras). All the data were acquired at the beginning of 2022 and refer to the areas of Boccasette and Barricata, in the Po River Delta (Northeastern of Italy). The real-time coastline obtained from the GNSS data was validated using the topographic total station measurements; the correspondent polylines obtained from the photogrammetric data (using both automatic extraction and manual restitutions by visual inspection of orhophotos) were compared with the GNSS data to evaluate the performances of the different techniques. The results provided good agreement between the real-time coastlines obtained from different approaches. However, using the optical images, the accuracy was strictly connected with the radiometric changes in the photos and using thermal images, both manual and automatic polylines provided differences in the order of 1–2 m. Multi-temporal comparison of the 0-level coastline with those obtained from a LiDAR survey performed in 2018 provided the detection of the erosion and accretion areas in the period 2018–2022. The investigation on the two case studies showed a better accuracy of the GNSS RTK method in the real-time coastline detection. It can be considered as reliable ground-truth reference for the evaluation of the photogrammetric coastlines. While GNSS RTK proved to be more productive and efficient, optical and thermal SfM provided better results in terms of morphological completeness of the data.

Semantic Segmentation of Urban Airborne LiDAR Point Clouds Based on Fusion Attention Mechanism and Multi-Scale Features

Semantic segmentation of point clouds provided by airborne LiDAR survey in urban scenes is a great challenge. This is due to the fact that point clouds at boundaries of different types of objects are easy to be mixed and have geometric spatial similarity. In addition, the 3D descriptions of the same type of objects have different scales. To address above problems, a fusion attention convolutional network (SMAnet) was proposed in this study. The fusion attention module includes a self-attention module (SAM) and multi-head attention module (MAM). The SAM can capture feature information according to correlation of adjacent point cloud and it can distinguish the mixed point clouds with similar geometric features effectively. The MAM strengthens connections among point clouds according to different subspace features, which is beneficial for distinguishing point clouds at different scales. In feature extraction, lightweight multi-scale feature extraction layers are used to effectively utilize local information of different neighbor fields. Additionally, in order to solve the feature externalization problem and expand the network receptive field, the SoftMax-stochastic pooling (SSP) algorithm is proposed to extract global features. The ISPRS 3D Semantic Labeling Contest dataset was chosen in this study for point cloud segmentation experimentation. Results showed that the overall accuracy and average F1-score of SMAnet reach 85.7% and 75.1%, respectively. It is therefore superior to common algorithms at present. The proposed model also achieved good results on the GML(B) dataset, which proves that the model has good generalization ability.