LiDAR Surveys Research Articles

Potential of the upcoming Biomass P-band radar mission for digital terrain modelling beneath dense tropical forests: first accuracy assessment

Abstract. P-band radar surveys can be used to produce digital elevation models (DEMs) at ground level in dense tropical forest areas. However, the previous performance studies were carried out on DEMs derived from high-resolution synthetic aperture radar (SAR) acquisitions, while the upcoming Biomass space mission should provide DEMs at a coarser resolution due to a frequency bandwidth of 6 MHz in range. The aim of this study is to predict the quality of the DEM products derived from Biomass data through dense forest. A DEM was produced in a slant range geometry at a 15-m mesh size using SAR tomography. It was compared to the reference LiDAR survey in terms of elevation and slope, and the statistical distribution of the corresponding error was analysed with regards to the radar off-nadir angle and to the local slope and aspect. The results show that the DEM elevation has no systematic error despite the presence of forest, and it has an RMS error of 3 meters. The DEM slope has a mean error of 2° and an RMS error of 6°. The behaviour of these errors for different terrain slopes and aspects will allow to predict the quality of Biomass DEMs on a variety of terrestrial landscapes.

Evaluation of the Usability of UAV LiDAR for Analysis of Karst (Doline) Terrain Morphology.

Traditional terrain analysis has relied on Digital Topographic Maps produced by national agencies and Digital Elevation Models (DEMs) created using Airborne LiDAR. However, these methods have significant drawbacks, including the difficulty in acquiring data at the desired time and precision, as well as high costs. Recently, advancements and miniaturization in LiDAR technology have enabled its integration with Unmanned Aerial Vehicles (UAVs), allowing for the collection of highly precise terrain data. This approach combines the advantages of conventional UAV photogrammetry with the flexibility of obtaining data at specific times and locations, facilitating a wider range of studies. Despite these advancements, the application of UAV LiDAR in terrain analysis remains underexplored. This study aims to assess the utility of UAV LiDAR for terrain analysis by focusing on the doline features within karst landscapes. In this study, we analyzed doline terrain using three types of data: 1:5000 scale digital topographic maps provided by the National Geographic Information Institute (NGII) of Korea, Digital Surface Models (DSMs) obtained through UAV photogrammetry, and DEMs acquired via UAV LiDAR surveys. The analysis results indicated that UAV LiDAR provided the most precise three-dimensional spatial information for the entire study site, yielding the most detailed analysis outcomes. These findings suggest that UAV LiDAR can be utilized to represent terrain features with greater precision in the future; this is expected to be highly useful not only for generating contours but also for conducting more detailed topographic analyses, such as calculating the area and slope of the study sites.

Expecting the expected – learning from the past to provide forward scenarios through geomorphic change detection, monitoring and modeling

This paper tells the story of a landslide, its origins, its activity and the actions undertaken to mitigate the risks that it poses. The Rotolon landslide is a large Deep-seated Gravitational Slope Deformation (DGSD), located in the Eastern Italian Alps, whose unremitting movements provide a sediment supply for large-scale debris flow events. It has been active since at least 1789, threatening the valley where the thermal baths town of Recoaro Terme is located. In 2010, a major reactivation of the landslide channelled 320,000 m3 of material towards the town causing significant concern, the evacuation of the exposed population and threatening several buildings. A few days after the emergency, the personnel of the Research Institute for Geo-Hydrological Protection of the Italian Research Council (IRPI-CNR) deployed a monitoring system consisting of an automatic total station, several extensometers and web cameras to monitor the evolution of the unstable slope and set up an early warning and alarm system equipped with sirens to warn the local population. Subsequently, after the emergency phase, the 2010 event was studied through multi-temporal LiDAR Digital Terrain Models (DTMs) and modeling. The authors have continued monitoring and actively studying this landslide ever since. In 2020, a new LiDAR survey of the area allowed assessment of the sediment dynamics within the catchment through the comparison with the post-2010 event LiDAR DTM. Based on DEM of difference maps, new insight into the behaviour of the catchment emerged, which appears to be influenced both by natural processes and anthropic activities. The authors were able to assess the amount of sediment that could be stored in the channel without causing overflooding and to calculate the expected damage to the built environment should another event occur. Furthermore, the considerable amount of data collected by the monitoring system throughout the years has allowed identification of two active sectors of the DGSD that may be prone to detachment, and through numerical modeling, future hazard scenarios were produced. Based on these outcomes, a second-tier targeted monitoring campaign was implemented, consisting of a network of four permanent GNSS receivers, additional topographic benchmarks and web cameras, and a new early warning protocol, in an ever-updating cycle of monitoring, modeling and mitigation.

Spatial distribution and deposits of cliffs along the Brazilian semi-arid coast

Cliffed coasts are common landforms found worldwide, but their precise spatial distribution and the nature of associated deposits may present regional gaps. This works presents a geological and geomorphological study of the Brazilian semi-arid coast (∼900 km), which was carried out on coastal cliffs with the aim of addressing three important knowledge gaps: i) spatial distribution of coastal cliffs, analyzed using topographic data from SRTM, LiDAR and field surveys, ii) the lithology and ages of the deposits, investigated through sedimentological analysis and OSL dating, and iii) origin of the cliffs, explored through relations with global and regional sea-level curves. The supply of terrigenous sediments to the western equatorial Atlantic coast was also considered. Coastal cliffs were identified along 235 km of the coastline, represented mainly by inactive cliffs (∼170 km). These cliffed coasts are composed mainly by poorly sorted siliciclastic sandstones and conglomerates, deposited in the Late Pleistocene. The depositional phases correlate with abrupt climate changes, due to pulses of terrigenous supply to the continental margin during the Heinrich Events. The formation of marine cliffs in these deposits occurred in the mid-Holocene, as sea level reached current elevation (msl) in ∼7 ka and 2-4 m above in ∼5.5 ka. This previous higher sea-level supports the widespread occurrence of inactive cliffs. The findings in this work contribute to i) improved mapping of coastal landforms, with higher resolution data demonstrating that previous works underestimated the presence of coastal cliffs; ii) the coastal cliffs are cut in Pleistocene sediments instead of older, Miocene deposits of the Barreiras Formation as previously assumed; iii) cliff formation due to marine erosion occurred in the mid- to- late Holocene; iv) the siliciclastic sediments that compose the cliffs are correlatable to marine sediments found in adjacent offshore areas, highlighting the role of abrupt global climatic changes in the Pleistocene in shaping contemporary coastal landscapes.

Factors and effects of inter-individual variability in silver birch phenology using dense LiDAR time-series

Comprehending and quantifying local variability in plant phenology, alongside its impacts on tree growth, is challenging due to spatially and temporally heterogeneous environmental factors that interact to affect phenological events and periods. Although previous studies have focused on the climatic factors driving phenological events at the stand level, the influences of competition, neighborhood characteristics, species richness, and water availability on plant phenology remain unclear. In the present paper, we used hourly terrestrial LiDAR surveys performed between April 2020 and April 2021 to investigate the influence of local factors (neighborhood and topography) on the phenology of silver birch trees (Betula pendula Roth.). We also examined how phenological events affect growth in tree height and crown area. All response and explanatory variables were estimated using LiDAR time-series data and a field survey campaign. Our findings demonstrate a high within-species variability in plant phenology that is controlled by biotic and abiotic characteristics of the ecosystem. We found that between tree variation in leaf burst, completion of the leaf growth and length of canopy growth period were affected by tree size, neighborhood species richness, level of suppression and competition, which potentially indicate plant responses to light availability. The beginning of senescence, completion of leaf drop, and length of autumn phenology were mostly affected by topographic water index, which reflects water availability and can be linked to nutrient availability and exposure to wind. Furthermore, we demonstrated that an earlier leaf burst, and delayed beginning of senescence were associated with larger absolute growth in canopy area but found no clear relationship between height growth and phenology. Our study highlights the capability of dense, high-quality LiDAR time-series to detect significant phenological variation among individuals of the same species within the same locality, demonstrating the potential of LiDAR time-series as a tool for understanding phenology and its impacts.

The rise of the Kaanuˀl kingdom and the city of Dzibanche

Abstract The Kaanuˀl dynasty ruled a hegemonic state with political influence over much of the Classic Maya Lowlands between a.d. 520 and 751. The present article introduces the subject for a special section of the journal, which refocuses attention on the archaeological zone of Dzibanche in southern Quintana Roo, Mexico, where new data are emerging about the origins of the Kaanuˀl dynasty, its urban organization, and its connections to neighboring centers. In this article, we present new data from a recent lidar survey as well as from previous work by Enrique Nalda's Instituto Nacional de Antropología e Historia (INAH) project to reevaluate Dzibanche's characteristics vis-à-vis its rise as a kingdom with far-reaching political influence. We complement these archaeological data with epigraphic information from new monuments and reanalysis of existing ones based on 3D scanning to update the list of Dzibanche rulers. We then revisit the chronology of Dzibanche's royal burials proposing correlations with known Early Classic Kaanuˀl rulers. Overall, the contributions to this special section present new perspectives on the Kaanuˀl's rise to power and its relationship with distant vassals in the crucial period of expansion into northern Peten, leading to the defeat of Tikal and eventually to its transition to a new dynastic seat at Calakmul in the a.d. 630s.

Running out of empty space: environmental lidar and the crowded ancient landscape of Campeche, Mexico

As airborne lidar surveys reveal a growing sample of urbanised tropical landscapes, questions linger about the sampling bias of such research leading to inflated estimates of urban extent and population magnitude. ‘Found’ datasets from remote sensing conducted for non-archaeological purposes and thus not subject to archaeological site bias, provide an opportunity to address these concerns through pseudorandom sampling. Here, the authors present their analysis of an environmental lidar dataset from Campeche, Mexico, which reveals previously unrecorded urbanism and dense regional-scale settlement. Both characteristics, the authors argue, are therefore demonstrably ubiquitous across the central Maya Lowlands.

Detection and automatic identification of loess sinkholes from the perspective of LiDAR point clouds and deep learning algorithm

Nowadays, the detection and automatic identification of the three-dimensional structure of sinkholes is extremely lacking, which has resulted in significant gaps in sinkholes mapping, soil erosion estimation and morphological studies. In this study, we discovered 249 sinkholes on a river terrace (about 2050 m long and 100 m wide) in a small watershed of Chinese Loess Plateau. Subsequently, we used the unmanned aircraft systems (UAS) and handheld laser scanner (HLS) to investigate these loess sinkholes in detail. We introduced the PointNet ++ deep learning model to train the point cloud dataset for 50 epochs and then selected the best model. In order to evaluate the identification accuracy and transferability of the model, we input point clouds of the unknown prediction area into the trained model to predict the sinkhole point clouds. The trained model exhibits excellent transferability and can effectively identify the sinkhole point clouds in the predicted area (OA = 0.935, IoU (Sinkhole) = 0.662, mIoU = 0.794, AUC = 0.966, Recognition rate = 82.46 %), and even sinkholes with complex connected structures can be accurately identified. This study provides a new perspective for future large-area LiDAR surveys, mapping, and assessment of sinkholes.

Improvement of the Evaluation of Seismic Risk in Fault Areas by Lidar-Derived Geophysical Data

This study aimed to improve the methods of assessing seismic risk in fault zones based on lidar data in geophysics. The research highlighted this by comparing the newly developed fault maps with the usual methods of fault mapping and how lidar technology developed high-resolution 3D mapping. We conducted mobile and terrestrial LIDAR surveys to produce DEMs and study the attributes of the fault zones. The technique involved mobile lidar systems with different specifications of emitted transmission rate: 45 000 m/s to 52, 100m/s pulse repetition: 190, 000 Hz–220, 000 Hz; and point density: 10223 points/m2 to 14567 points/m2. Terrestrial lidar surveys used scanner heights of 1. 500-1. 700m and obtained the horizontal and vertical sampling density, ranging from 240,456 to 315,678 points per square meter. We used LAStools, Arc GIS, and QISIS software to filter, classify, and visualize the data processing. e applied interpolation techniques such as IDW, Kriging, Spline, and Natural Neighbors to generate DEMs. Research outcomes identified 15 different fault segments with lengths varying from 10. 000-20. 000 km, along with maximum displacements of 0. 987-4. 567 m, and average slip rates of 3. 456-7. 890 mm/year. The most extended fault segment altogether was FS05, which was 20. 000 km with a maximum bidding distance of 4. 567 m and a 7. 890 mm/year slip rate. We discovered that the proposed method successfully filtered out noise points from lidar data, with the noise points varying between 0.111-0.266 million. We created DEMs with vertical rms errors ranging from 0.045-0.050 m. The study revealed that lidar technology offers accurate and dense geospatial data, essential for discriminating between fault zones. This approach dramatically improves seismic hazard analysis and the identification of the best ways to minimize risks. These are increasing lidar surveys in other seismically active regions, using multiple data sources for analysis, and deploying constant surveys in high-risk fault line regions to increase consistency in detecting surface changes and tectonic activity.

A national-scale high-resolution runoff risk and channel network mapping workflow for diffuse pollution management

Managing diffuse pollution from agricultural land requires a spatially explicit risk assessment that can be applied over large areas. Major components of such assessments are the precise definition of both channel networks that often originate as small channels and streams, and Hydrologically Sensitive Areas (HSAs) of storm runoff that occur on land surfaces. Challenges relate to regions of complex topography and land use patterns, particularly those which have been heavily modified by arterial drainage. In this study, a national scale, transferrable workflow and analysis were developed using a specifically commissioned LiDAR survey. Research on the first half of Northern Ireland (6927 km2) is reported where field-edge drain to major river channels were mapped from 1 m (16 points per metre) digital terrain models, and in-field HSAs were defined across over 400,000 fields with a median field size of 0.86 ha. Manual drainage mapping supplemented with a novel automated drainage channel correction process resulted in an unparalleled high-resolution national drainage network with 37,320 km of channels, increasing mapped channel density from 0.9 km km−2 to 5.5 km km−2. The HSAs were based on a Soil Topographic Index (STI) system using hillslope and contributing area models combined with soil hydraulic characteristics. In all, 249 km2 of runoff risk HSAs were identified by extracting the top 95th percentile of the modelled STI as the areas with the highest propensity to generate in-field runoff. At field and individual farm scale these targeted risk maps of diffuse pollution were delivered to over 13,000 farmers and form part of the nationwide Soil Nutrient Health Scheme programme.

Modeling dominant height with USGS 3DEP LiDAR to determine site index in even-aged loblolly pine (Pinus taeda L.) plantations in the southeastern US

Abstract Accurate quantification and mapping of forest productivity are critical to understanding and managing forest ecosystems. Local LiDAR or photogrammetric surveys have been used to obtain reliable estimates of canopy heights, yet these acquisitions can entail substantial expenses. Therefore, we developed models using freely available US Geological survey (USGS) LiDAR data for prediction of dominant height to map site index across loblolly pine (Pinus taeda L.) plantations in the southeastern US. We used 2017–2020 national USGS 3D Elevation Program LiDAR acquisitions and explored how different height percentiles, grid output resolutions, time difference between LiDAR and ground acquisitions, tree height, and dominant height definition affected the proposed model. We built the dominant height models using 1301 ground plots. The final regression model was constructed with the 95th percentile of the height distribution of the first returns above-ground and had values of R2 = 0.89, RMSE = 1.55 m, and RRMSE = 7.66 per cent at a 20-m pixel grid, yet all the examined percentile-resolution combinations were acceptable. No effect evidence was found for time difference when the flight was less than 4 months in advance or after the ground measurement, and it was also found independent of pulse density when this variable was lower than 9.5 pulses m−2. Using the recorded age of the plantations, we assessed the error propagation when translating dominant height to site index in two site index models, obtaining an RRMSE lower than 10 per cent in both. We found that USGS LiDAR acquisitions can be reliably used to map dominant height at a large scale, and consequently used to map forest productivity when age is known. This ability adds more value to a tool proven widely applicable in time and space and offers a great opportunity for stakeholders in different fields of use.

On the Move: 2023 Observations on Real Time Graben Formation, Grindavík, Iceland

AbstractGrabens, or valleys formed during extensional tectonic events, are common but rarely observed during formation. In November 2023, inelastic surface deformation formed abruptly along Iceland's plate boundary in Grindavík. We documented graben formation in real‐time through satellite mapping (InSAR), seismicity, GNSS data, repeated lidar surveys, and field mapping. Five normal faults and ∼12 fissures ruptured the surface delineating two grabens separated by a horst, a context not present in other contemporary case studies. The graben normal faults slipped rapidly (over hours) and maximum surface motions coincided with the occurrence of turbulent seismic swarms in both space and time. Although 3 eruptions took place ∼15 km northeast of Grindavík from 2021 to 2023, attributed to magma intrusions (i.e., dikes), none of these also formed grabens. Thus, the Grindavík grabens shows evidence for tectonic origins. Real‐time monitoring of these phenomena provide insight into graben formation on Earth and potentially on other planets.

Seasonal optimisation of drone‐based photogrammetry in a heterogeneous boreal landscape

AbstractAimsUncrewed aerial vehicles (UAV), or drones, have become more affordable and easier to use, resulting in increased UAV applications in ecology and conservation. However, solar illumination, vegetation phenology and prevailing weather conditions will impact the quality of the derived products to differing degrees. In this study, we investigate how seasonal differences in solar illumination, tree foliage and weather conditions impact the accuracy of digital elevation models (DEM) and canopy height models (CHM) in a heterogeneous boreal landscape.MethodsWe compared DEMs and CHMs derived from drone photogrammetry with DEMs and CHMs produced from a drone‐mounted laser scanner across three seasons with different solar illumination, tree foliage and weather conditions during leaf‐off and leaf‐on seasons. Photogrammetric height models were evaluated across three land‐cover classes consisting of open areas, sparse‐forest and forest. The most accurate CHM for sparse‐forest was produced during summer under overcast conditions, whereas for the forest class, summer under clear skies was best.ResultsStructure from motion (SfM) photogrammetry performed well against the LiDAR survey in most cases with correlations between sampled points of up to R2 = 0.995. Root mean square errors (RMSEs) were <1.5 m in all DEMs and as low as 0.31 m in autumn clear‐sky data over open terrain. CHM RMSEs were somewhat higher in all cases except under winter overcast conditions when the RMSE for sparse‐forest reached 6.03 m.ConclusionsWe have shown that SfM photogrammetry is surprisingly robust to variations in vegetation type, tree phenology and weather, and performs well in comparison with a reference LiDAR data set. Our results show that, in boreal forests, autumn is the preferred season under clear‐sky conditions for DEM generation from SfM photogrammetry across all land‐cover classes, whereas summer is preferred for CHM modelling with a small trade‐off between overcast and clear‐sky conditions over different vegetation types. These results can help potential SfM users in ecology and forestry plan missions and review the quality of products derived from drone photogrammetry products.

Chapter 6. Found field: Encountering a ridged garden bed archaeological site, Wing Reach, in Wisconsin, USA

AbstractRaised garden beds were once among the most common Native American earthworks in eastern North America. Typically located on prime agricultural land, they are now among the rarest. However, previously unrecorded archaeological raised beds can still be uncovered, especially in more marginal agricultural settings. This chapter details the discovery of a previously unrecorded ancestral Native American ridged agricultural field site in Juneau County, Wisconsin, USA. The site was first identified in 2020 by the authors using publicly available historical aerial imagery and a recent lidar survey. Here we describe its confirmation as a ridged field archaeological site and emplace it within broader anthropogenic landscapes of Wisconsin. Methods described herein can be employed globally to locate and document raised bed agriculture.

Long-term annual estimation of forest above ground biomass, canopy cover, and height from airborne and spaceborne sensors synergies in the Iberian Peninsula

The Mediterranean Basin has experienced substantial land use changes as traditional agriculture decreased and population migrated from rural to urban areas, which have resulted in a large forest cover increase. The combination of Landsat time series, providing spectral information, with lidar, offering three-dimensional insights, has emerged as a viable option for the large-scale cartography of forest structural attributes across large time spans. Here we develop and test a comprehensive framework to map forest above ground biomass, canopy cover and forest height in two regions spanning the most representative biomes in the peninsular Spain, Mediterranean (Madrid region) and temperate (Basque Country). As reference, we used lidar-based direct estimates of stand height and forest canopy cover. The reference biomass and volume were predicted from lidar metrics. Landsat time series predictors included annual temporal profiles of band reflectance and vegetation indices for the 1985–2023 period. Additional predictor variables including synthetic aperture radar, disturbance history, topography and forest type were also evaluated to optimize forest structural attributes retrieval. The estimates were independently validated at two temporal scales, i) the year of model calibration and ii) the year of the second lidar survey. The final models used as predictor variables only Landsat based metrics and topographic information, as the available SAR time-series were relatively short (1991–2011) and disturbance information did not decrease the estimation error. Model accuracies were higher in the Mediterranean forests when compared to the temperate forests (R2 = 0.6–0.8 vs. 0.4–0.5). Between the first (1985–1989) and the last (2020–2023) decades of the monitoring period the average forest cover increased from 21 ± 2% to 32 ± 1%, mean height increased from 6.6 ± 0.43 m to 7.9 ± 0.18 m and the mean biomass from 31.9 ± 3.6 t ha−1 to 50.4 ± 1 t ha−1 for the Mediterranean forests. In temperate forests, the average canopy cover increased from 55 ± 4% to 59 ± 3%, mean height increased from 15.8 ± 0.77 m to 17.3 ± 0.21m, while the growing stock volume increased from 137.8 ± 8.2 to 151.5 ± 3.8 m3 ha−1. Our results suggest that multispectral data can be successfully linked with lidar to provide continuous information on forest height, cover, and biomass trends.

Drones and Real-Time Kinematic Base Station Integration for Documenting Inaccessible Ruins: A Case Study Approach

Ruins, marked by decay and abandonment, present challenges for digital documentation due to their varied conditions and remote locations. Surveying inaccessible ruins demands innovative approaches for safety and accuracy. Drones with high-resolution cameras enable the detailed aerial inspection and imaging of these inaccessible areas. This study investigated how surveying technologies, particularly Unmanned Aerial Vehicles (UAVs), are used to document inaccessible ruins. Integration with Real-Time Kinematic (RTK) technologies allows direct georeferencing in photogrammetric processing. A case study of the Castle of Terracorpo in Italy was used to demonstrate UAV-only surveying feasibility in inaccessible environments, testing two different scenarios. The first scenario involved the use of a DJI Matrice 300 RTK coupled with the D-RTK2 base station to survey the Castle; both direct and indirect georeferencing were exploited and compared through the photogrammetric process. This first scenario confirmed that this approach can lead to a centimetre-level accuracy (about three times the GSD value for indirect georeferencing and seven times the GSD value for direct georeferencing exploting RTK). The second scenario testing the integration of data from drones at varying resolutions enabled the comprehensive coverage of ruinous structures. In this case, the photogrammetric survey performed with the dji Mavic 3 Cine drone (indirect georeferencing) was integrated with the photogrammetric survey performed with the dji Matrice 300 RTK drone (direct georeferencing). This scenario showed that GCPs extracted from a direct georeferencing photogrammetric survey could be successfully used to georeference and integrate other drone data. However, challenges persist in surveying underground or enclosed spaces that are inaccessible to UAVs. Future research will explore integrating robotic LiDAR survey systems and advanced data fusion techniques to enhance documentation.

Is ward-level calculation of urban green space availability important?—A case study on Vellore city, India, using the histogram-based spectral discrimination approach

How much green space is available for individuals is a major question that city planners are generally interested in, and the present study aimed to address this issue in the context of Vellore, India, through two approaches, namely, the per capita and the geographical area approach. In existing studies, urban green space (UGS) was only calculated at the macro level, i.e., for the city as a whole. Micro-or ward-level analysis was not attempted before, and the present study carried out the same to get a clear picture of the amount of greenery available in each ward of a city. For this purpose, a two-step approach was proposed where the histograms of Google Earth (GE) images were analyzed first to check whether the green cover types such as trees, shrubs/grassland, and cropland were spectrally different. Then, classification techniques such as ISODATA, maximum likelihood, support vector machine (SVM), and object-based methods were applied to the GE images. It was found that SVM performed well in extracting different green cover types with the highest overall accuracy of 93% and Kappa coefficient of 0.881. It was found that when considering the city as a whole, the amount of UGS available is 42% of the total area, which is more than the recommended range of 20–40%. Similarly, the available UGS per person is 97.84 m2, which is far above the recommended 12 m2/person. However, the micro-level analysis revealed that some of the wards have not satisfied the criteria of per capita and percentage area, though the city as a whole has satisfied both the criteria. Thus, the results indicate the importance of calculating the urban green space availability at the ward level rather than the city level as the former gives a closer look at the surplus and deficit areas. The results of terrestrial LiDAR survey at individual tree level revealed that if trees are located adjacent to buildings or roads, it results in fewer heat islands compared to the case where there are no trees.

Rockfall susceptibility analysis of the “San Michele Arcangelo” historic trail (Central Italy) based on virtual outcrops and multiple propagation models

The “San Michele Arcangelo” historic trail (Aurunci Regional Park, central Italy), located along the southern slope of Mt. Altino, which is highly prone to rockfalls, is hiked every year by thousands of faithful on pilgrimage who are exposed to such kinds of instabilities. To contribute to a better understanding of the condition and evolution of such phenomena, providing a susceptibility scenario able to support the adoption of mitigation measures, a specific analysis was completed based on field and literature data. Three-dimensional virtual outcrop models were obtained through both photogrammetric and iPad Pro LiDAR surveys to derive geomechanical features of outcropping rocks and estimate potential detaching block volume. Possible mechanisms of detachment were then analyzed using the Markland test method. Susceptibility to rockfall propagation and block deposition was analyzed using GIS processing and rockfall propagation simulations based on deterministic/stochastic models. Such models consist of a combination of a deterministic algorithm able to simulate the physics of rockfall movement during propagation and a stochastic treatment of input parameters based on random sampling within defined interval. Two different rockfall simulations were compared in this study using the Rockyfor3D™ (ecorisQ Association) software and the newly developed RocFall3© (RocScience) code. The use of iPad Pro LiDAR survey provides accurate and high-resolution point clouds, with high speed of acquisition and real-time processing of data. Discontinuity sets identified from 3D models have represented the input for kinematic analyses of slopes, allowing to identify potential failure mechanisms among wedge and planar sliding or toppling. Rockfall simulations indicate the potential for rock blocks propagation and deposition over the whole study area. Comparing the results of rockfall simulations with the geomorphological map of the area it is evident the control exerted by the hydrographic network on rockfall propagation as demonstrated by the presence of screes and slope deposits along the main channels. Understanding the potential susceptibility to both propagation and deposition of rock blocks along the trail provides useful indication for the assessment of appropriate mitigation measures to realize for the safer touristic fruition of the site.

Increased habitat availability as revealed by LiDAR contributes to the tropicalization of a subtropical butterfly community

AbstractSpecies redistribution through climate change remains a global problem. However, factors such as habitat availability can complicate the attribution of species distribution shifts. We used habitat metrics derived from repeated airborne LiDAR surveys in 2010 to 2020 to examine the underlying causes for the establishment of new butterfly species in Hong Kong. For six species newly arrived since 2000, we built species distribution models using the Random Forest algorithm based on LiDAR data from 2020 to characterize species' preferred habitats across the region. Through hindcasting, we applied the model to LiDAR data from 2010 to observe any changes in the availability of preferred habitat. We found widespread vertical forest growth across Hong Kong and increased probability of occurrence based on increased habitat availability for all six species. The underlying habitat drivers, however, varied significantly across species; two species (Lethe chandica, Notocrypta paralysos) were associated with closed forest while two other species (Prosotas dubiosa, Prosotas nora) were associated with urbanicity. Our results highlight how changes in habitat can occur concurrently with climatic change and together drive the redistribution of biodiversity. Particularly for vertically complex tropical forests, airborne LiDAR data can be leveraged to observe changes in habitat complexity and how these relate to shifts in species distributions.

Four Years of Meander‐Bend Evolution Captured by Drone‐Based Lidar Reveals Lack of Width Maintenance on the White River, Indiana, USA

AbstractMeandering rivers experience fluctuations in width whenever riverbanks migrate in different directions or at different rates, which can be observed after individual floods. However, meandering rivers maintain approximately constant widths over decadal timescales. This implies some timescale below which width fluctuates as banks migrate independently, and above which width is maintained by a bank‐coupling process. This coupling is thought to occur either as point bar deposition events induce cutbank erosion (bar‐push), or as cutbank erosion events induce point bar deposition (bank‐pull). This coupling, however, has been challenging to observe in natural rivers due to limited event‐scale field data. We present results from a 4.5‐year campaign with 22 drone‐based lidar surveys of a single point bar and cutbank (∼0.35 km2 in area) on the White River near Worthington, Indiana, USA. The middle point bar experienced net erosion (5,400 m3), but net aggradation (17,100 m3) between 2019 and 2022 when including perennially submerged regions. This aggradation was less than the 35,700 m3 of cutbank erosion over the same period. Combined, we have observed widening (1.58 m/yr bend‐averaged; 3.08 m/yr near apex) over the study period as point bar deposition has not kept up with cutbank erosion. Finally, we suggest that the difference between bar‐push and bank‐pull as width‐maintenance mechanisms may not be resolvable by observing bend widening or narrowing alone without an advancement of current theory, such as determining a long‐term equilibrium width and measuring deviations relative thereto.