Light Detection And Ranging Survey Research Articles

Improving the Accuracy of Digital Terrain Models Using Drone-Based LiDAR for the Morpho-Structural Analysis of Active Calderas: The Case of Ischia Island, Italy

Over the past two decades, the airborne Light Detection and Ranging (LiDAR) system has become a useful tool for acquiring high-resolution topographic data, especially in active tectonics studies. Analyzing Digital Terrain Models (DTMs) from LiDAR exposes morpho-structural elements, aiding in the understanding of fault zones, among other applications. Despite its effectiveness, challenges persist in regions with rapid deformation, dense vegetation, and human impact. We propose an adapted workflow transitioning from the conventional airborne LiDAR system to the usage of drone-based LiDAR technology for higher-resolution data acquisition. Additionally, drones offer a more cost-effective solution, both in an initial investment and ongoing operational expenses. Our goal is to demonstrate how drone-based LiDAR enhances the identification of active deformation features, particularly for earthquake-induced surface faulting. To evaluate the potential of our technique, we conducted a drone-based LiDAR survey in the Casamicciola Terme area, north of Ischia Island, Italy, known for the occurrence of destructive shallow earthquakes, including the 2017 Md = 4 event. We assessed the quality of our acquired DTM by comparing it with existing elevation datasets for the same area. We discuss the advantages and limitations of each DTM product in relation to our results, particularly when applied to fault mapping. By analyzing derivative DTM products, we identified the fault scarps within the Casamicciola Holocene Graben (CHG) and mapped its structural geometry in detail. The analysis of both linear and areal geomorphic features allowed us to identify the primary factors influencing the current morphological arrangement of the CHG area. Our detailed map depicts a nested graben formed by two main structures (the Maio and Sentinella faults) and minor internal faults (the Purgatorio and Nizzola faults). High-resolution DEMs acquired by drone-based LiDAR facilitated detailed studies of the geomorphology and fault activity. A similar approach can be applied in regions where the evidence of high slip-rate faults is difficult to identify due to vegetation cover and inaccessibility.

Application of Drone LiDAR Survey for Evaluation of a Long-Term Consolidation Settlement of Large Land Reclamation

Drone surveys are gaining popularity for many construction applications, including in the fields of civil engineering, such as road construction, earthwork, structure monitoring, and coastal topography analysis. Drone surveying has a high potential for periodical long-term ground settlement measurement in the field of geotechnical engineering. Traditionally, manual measurement has been performed for limited points with controlled surface measurement points, but drone surveying may enable automated and periodical measurement for a wide and remote site. However, the accuracy of the elevation measurement and the surface settlement prediction has not been investigated, and the use of drone surveying has thus been limited. Therefore, an experiment was carried out to apply drone LiDAR (Light Detection and Ranging) surveying for soft ground settlement measurement at a large land reclamation site showing a very large settlement up to 10 m. Periodic drone LiDAR surveying was conducted, and the data were processed with direct georeferencing and with outlier removals (such as trees and construction vehicles) in order to generate a clean surface point cloud. We then compared the processed elevation data with ground control data to check the vertical accuracy and to predict the settlement as well as for consolidation. The drone LiDAR survey showed 13 cm, 42.9 cm, and 6.23% differences in RMSE (Root Mean Square Error) in terms of vertical accuracy, predicted long-term settlement, and consolidation, respectively. The drone LiDAR accuracy seems very useful for monitoring settlement over a large and remote land reclamation site of soft ground, showing settlement up to several meters where, without a surface measurement, installment is limited.

SETTLEMENT, AGRICULTURE, AND ENVIRONMENT AT AVENTURA, BELIZE: RESULTS OF NEW LIDAR RESEARCH

In 2009, the first LiDAR (Light Detection and Ranging) was flown over Caracol, Belize, transforming our understanding of Maya settlement, agriculture, and environment, and positioning the country of Belize as a leader in this transformation. Flown a decade later in 2019, this article reports on an 18 square kilometer LiDAR survey at Aventura, northern Belize. The National Center for Airborne Laser Mapping at the University of Houston used an Optech Titan sensor, the world’s first multispectral airborne LiDAR sensor, in the Aventura LiDAR survey. With approximately 25 points per square meter, we were able to maximize our detection of smaller features, as documented in ground truthing. LiDAR has two primary uses: (1) locating previously unidentified sites and (2) providing a complex human geography of ancient places that link people and land. This paper highlights how LiDAR facilitates the development of a human geography of ancient places. At Aventura, LiDAR research illustrates a human geography that links people, settlement, agricultural, and environment. Raised field agricultural systems at Aventura along the New River, and systems of bajos and pocket bajos, provide a window into understanding Aventura’s environmental positioning, wetland resources, and agrarian roots and insight into a broader New River agricultural-environmental system.

Millennial-scale coseismic landslide history inferred from topographic and stratigraphic features of a post-caldera cone of Aso Volcano in southwestern Japan

The 2016 Mw 7.0 Kumamoto earthquake triggered numerous landslides of fallout tephra deposits around Aso Volcano in southwestern Japan. Although rainfall-induced landslides have been repeatedly observed on the tephra-mantled slopes of this region, the spatial patterns and frequency of coseismic landslides remain unclear because of the scarce landslide evidence associated with palaeoseismicity. To examine the distribution of landslides induced by the 2016 Kumamoto earthquake and possibly by prior large earthquakes, we conducted geomorphological analyses using airborne light detection and ranging (LiDAR) data, together with geological investigations of tephra deposits in the Takanoobane lava dome (TLD), a post-caldera cone of Aso Volcano. Repeated LiDAR surveys performed before and after the earthquake allowed us to quantify the volume and geometry of five coseismic landslides (K1–K5), as well as three component blocks of the K1 landslide (K1a–c). The surveys also allowed identification of 12 older landslides (O1–O12) fully covered by vegetation. Source areas of recent coseismic landslides ranged from 1050 to 14,920 m2; those of older landslides were estimated to be 1110–15,730 m2. The H/L ratios of the K1 landslide and its component blocks K1a–c ranged from 0.139 to 0.165, despite their volumes ranging from 104 to 105 m3. The K1 landslide occurred on the southwest-facing slope, where older landslides were not recognised before the earthquake. Its slip surface was formed at the base of the 30 ka pumice layer. The largest one of the older landslides (O1) is located on the south-facing slope, adjacent to the K1 landslide. Stratigraphic investigations revealed that most of the tephra sequence, including the 30 ka pumice layer, has already been eroded at the centre of the O1 landslide. Radiocarbon dating further constrained the landslide age to ca. 7.0 ka. Because its topographic and stratigraphic features are similar to the features of the K1 landslide, the O1 landslide may have been triggered by a palaeoearthquake approximately 7000 years ago, which was previously unknown around the Aso caldera region.

Metaheuristics for the optimization of Terrestrial LiDAR set-up

3D point clouds have a significant impact on a wide range of applications, although their acquisition is frequently conditioned by the occlusion of the objects in the scene. To address this problem, this paper describes an approach for optimizing LiDAR (Light Detection and Ranging) surveys using metaheuristics such as local searches and genetic algorithms. The method generates a set of optimal scanning locations to densely cover the real-world environment represented through 3D synthetic models. Compared to previous research, this paper handles 3D occlusion by varying the height of the sensor. Also, previously used metrics are compressed into three functions to avoid multi-objective optimization. Regarding performance, a LiDAR scanning solution based on GPU (Graphics Processing Unit) hardware is used. Several tests were conducted to show that the combination of local searches and genetic algorithms generates a reduced set of locations capable of optimizing the scanning of buildings.

Laserfarm – A high-throughput workflow for generating geospatial data products of ecosystem structure from airborne laser scanning point clouds

Quantifying ecosystem structure is of key importance for ecology, conservation, restoration, and biodiversity monitoring because the diversity, geographic distribution and abundance of animals, plants and other organisms is tightly linked to the physical structure of vegetation and associated microclimates. Light Detection And Ranging (LiDAR) — an active remote sensing technique — can provide detailed and high resolution information on ecosystem structure because the laser pulse emitted from the sensor and its subsequent return signal from the vegetation (leaves, branches, stems) delivers three-dimensional point clouds from which metrics of vegetation structure (e.g. ecosystem height, cover, and structural complexity) can be derived. However, processing 3D LiDAR point clouds into geospatial data products of ecosystem structure remains challenging across broad spatial extents due to the large volume of national or regional point cloud datasets (typically multiple terabytes consisting of hundreds of billions of points). Here, we present a high-throughput workflow called ‘Laserfarm’ enabling the efficient, scalable and distributed processing of multi-terabyte LiDAR point clouds from national and regional airborne laser scanning (ALS) surveys into geospatial data products of ecosystem structure. Laserfarm is a free and open-source, end-to-end workflow which contains modular pipelines for the re-tiling, normalization, feature extraction and rasterization of point cloud information from ALS and other LiDAR surveys. The workflow is designed with horizontal scalability and can be deployed with distributed computing on different infrastructures, e.g. a cluster of virtual machines. We demonstrate the Laserfarm workflow by processing a country-wide multi-terabyte ALS dataset of the Netherlands (covering ∼34,000 km2 with ∼700 billion points and ∼ 16 TB uncompressed LiDAR point clouds) into 25 raster layers at 10 m resolution capturing ecosystem height, cover and structural complexity at a national extent. The Laserfarm workflow, implemented in Python and available as Jupyter Notebooks, is applicable to other LiDAR datasets and enables users to execute automated pipelines for generating consistent and reproducible geospatial data products of ecosystems structure from massive amounts of LiDAR point clouds on distributed computing infrastructures, including cloud computing environments. We provide information on workflow performance (including total CPU times, total wall-time estimates and average CPU times for single files and LiDAR metrics) and discuss how the Laserfarm workflow can be scaled to other LiDAR datasets and computing environments, including remote cloud infrastructures. The Laserfarm workflow allows a broad user community to process massive amounts of LiDAR point clouds for mapping vegetation structure, e.g. for applications in ecology, biodiversity monitoring and ecosystem restoration.

Evaluation of LiDAR Point Clouds Density in the Interpolation of Digital Terrain Models for Power Line Planning in Northeast Brazil

Mapping activities for the implementation of basic and executive projects for electric power transmission lines systematically involves topographic surveys. This study presents the results of a comparative study on the influence of the density of aerial survey points, LiDAR (Light Detection and Ranging), for the calculation of digital terrain models (DTMs) in typical areas of the northeastern region of Brazil, in the context of modeling ground for planning electricity transmission lines. The study area is the region covered by the 230 kV Ibicoara/Brumado transmission line, located in the state of Bahia/Brazil.. In part of this region, elevations of the mountains of the Western Edge of Chapada Diamantina predominate. Therefore, points with varying densities were used to interpolate DTMs in different environments. Point cloud classification (Airborne LiDAR) was performed using the TerraScan program using semi-automatic classification methods, followed by manual refinements. To evaluate the terrain models obtained with different point densities, four areas were selected under transmission line: Area 1 (24.4/m2), Area 2 (46.0/m2), Area 3 (33.7/m2) e Area 4 (29.7/m2). The Airborne LiDAR survey was performed with an Optech ALTM Pegasus HD500 sensor, calibrated to obtain a density of 15 points/m2. The results showed that, depending on the nature of the vegetation cover, less dense laser surveys do not offer enough quality to generate DTMs. However, in some cases, when the vegetation is denser and the terrain is not flat, the quality of the DTM decreases as the density of the points decreases. The study shows that the survey density must be suitable for the region to be analyzed. An important guideline would be to repeat this study in other areas with different coverage and relief variations, in order to create specifications to serve as a basis for planning LiDAR surveys on other transmission lines.

Potential and limitations of LiDAR altimetry in archaeological survey. Copper Age and Bronze Age settlements in southern Iberia

Potential and limitations of LiDAR altimetry in archaeological survey. Copper Age and Bronze Age settlements in southern Iberia

A transient, perched aquifer model for banana hole formation: Evidence from San Salvador Island, Bahamas

AbstractBanana holes are karst depressions that have primarily been reported from strandplains within the Bahamian archipelago. Banana holes have been hypothesized to form by downward dissolution in the vadose zone and in the phreatic zone by mixing dissolution and/or spatial variability in organic carbon inputs to the water table. While vadose models have been unable to explain overhanging roofs common in banana holes, phreatic models require anomalously high dissolution rates. In this study, we develop a new model for banana hole formation based on field observations, an airborne LiDAR (light detection and ranging) survey, and published geochemical data on San Salvador Island, Bahamas. We detected 3356 depressions in LiDAR data consistent with banana hole morphologies in Marine isotope substage (MIS) 5e and MIS 9/11 strandplains. No banana holes were detected in Holocene strandplains. All banana holes were found in swales between ridges. Of the banana holes found in MIS 5e strandplains, 109 had floor elevations between 6 and 19 m above sea level. These banana holes could not have formed in the phreatic zone because the MIS 5e freshwater lens reached a maximum elevation of 6 m above modern sea level. We also observed banana holes filled with water during wet seasons. While wells next to banana holes were tidal, pools in the banana holes were not, indicating pools were perched. Our observations, supported by hydrological models, suggest banana holes may form in the vadose zone in transient, perched aquifers on exposure zones. Runoff from ridges infiltrates through vadose fast‐flow routes until encountering low permeability exposure zones, where flow is directed laterally. Dissolution by perching on exposure zones would create thin, laterally‐extensive chambers radiating from injection points. Subsequent roof collapse results in their surface expression. Because dissolution occurs when waters become perched, chambers could form throughout low stands and may therefore not reflect rapid phreatic dissolution.

Land-Surface Quantitative Analysis to Investigate the Spatial Distribution of Gravitational Landforms along Rocky Coasts

The increasing availability of high-quality digital elevation models (DEMs) has been associated with a growing interest in developing quantitative analyses aimed at taking advantage of these detailed, updated, and promising digital datasets. Land-surface quantitative (LSQ) analysis is valuable for describing the land-surface topography and performing measures of the signature of specific geomorphic processes, taking into account site-specific geological contexts and morphoclimatic settings, proving to be particularly effective in transitional environments, such as rocky coasts. This paper presents the results of research aimed at investigating the spatial distribution of gravitational landforms along rocky coasts, by means of LSQ analysis based on a DEM with a ground resolution of 2 m, derived from airborne LiDAR (light detection and ranging) surveys. The study area is at Mt. San Bartolo (Northern Marche, Italy) and characterized by a sea cliff diffusely affected by gravitational phenomena of different sizes and types. Geomorphological and geological field data, interpretations of remotely sensed datasets derived from ad hoc unmanned aerial vehicle (UAV) flights, and DEM-derived hillshades were also adapted to support LSQ analysis. In detail, four morphometric variables (slope, roughness, terrain ruggedness index, and elevation standard deviation) were computed and the outputs evaluated based on visual–spatial inspections of derived raster datasets, descriptive statistics, and joint comparison. Results reveal the best performing variables and how combined interpretations can support the identification and mapping of zones characterized by varying spatial distribution of gravitational landforms of different types. The findings achieved along the Mt. San Bartolo rocky coast confirm that an approach based on land-surface quantitative analysis can act as a proxy to efficiently investigate gravitational slope processes in coastal areas, especially those that are difficult to reach with traditional field surveys.

Effect of Turbidity, Temperature and Salinity of Waters on Depth Data from Airborne LiDAR Bathymetry

The influence of seawater parameters cannot be ignored when conducting bathymetric LiDAR (Laser Induced Detection and Ranging or Light Detection and Ranging) surveys such as turbidity, temperature, and salinity. Turbidity affects the attenuation diffusion coefficient of the green laser is penetrating the air column. The comparison of LiDAR bathymetric depth with Secchi disk depth is used as a reference in determining the effect of turbidity. The results are in locations with primarily clear water the ability of LiDAR can penetrate up to 7m, while in turbid waters up to 3m. On average, the ability of the green laser LiDAR bathymetry can penetrate the waters of 1.5-2 times the depth at the location of this study around the bay of Lampung Indonesia. Other water parameters are temperature and salinity. These parameters are used to calculate the refractive index value of water. The Different temperature and salinity values in a water column can result in differences in the accuracy of the bathymetry LiDAR depth of 4-6mm. The influence of water column parameters can be a concern in planning and processing airborne LiDAR altimetry (ALB) surveys.

Tidal Creek Mapping from Airborne LiDAR Data Using Multi-resolution Cloth Simulation Filtering

Kim, H.; Lee, J., and Kim, Y., 2021. Tidal creek mapping from airborne LiDAR data using multi-resolution cloth simulation filtering. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 86–90. Coconut Creek (Florida), ISSN 0749-0208. Tidal creeks are transitional waterways promoting the evolution and expansion of tidal flats. The precise and objective delineation of tidal creeks is critical for monitoring the characteristics, formation, and evolution of tidal flats. Airborne light detection and ranging (LiDAR) data are the most widely used source of tidal topography information, since they can provide precise terrain information over wide areas. However, existing tidal creek extraction methods using airborne LiDAR data have limitations, such as the necessity of excessive user intervention and a lack of adaptability to the various shapes and widths of tidal creeks. The morphological irregularities, complexity, and diverse widths (from a few centimeters to several kilometers) of tidal creeks, complicates their automatic or manual extraction from LiDAR data. In this study, we propose an effective and practical method for the mapping of tidal creeks with a wide range of widths. Here, cloth simulation filtering (CSF), a verified ground filtering technique used to filter off-ground objects from point cloud in land LiDAR surveys, was adopted and modified. By sequentially filtering the creek points through a hypothetical cloth with an increasingly larger grid resolution, the proposed multi-resolution CSF can extract huge tidal creeks without compromising the details of narrow creeks. This sequential filtering is based on local thresholds calculated using the creek points extracted during the previous filtering and does not require empirical parameter adjustments. The results of an experimental evaluation based on airborne LiDAR data collected over the west coast of South Korea indicate that the accuracy of the proposed method is high (Kappa > 0.8) and superior to that of results obtained through an user parameter.

Espacios agrarios, asentamientos prehispánicos y tecnología LiDAR en el área costera central del Ecuador

<p class="VARAbstract">The use of Light Detection and Ranging (LiDAR) technology has revolutionised pre-Hispanic archaeology in large tracts of Central America (Canuto et. al. 2018; Fisher et al. 2016; Garrison et al. 2019; Chase & Weishampel, 2016). This contribution is particularly significant in areas in which jungles and a complex orography had precluded the implementation of large-scale landscape archaeology studies.</p><p class="VARAbstract">Although this technology has not been used in South America as intensively as in Central America (Mexico, Guatemala and Honduras, among others), LiDAR is beginning to be used to answer questions concerning the interaction between the landscape, pre-Hispanic societies and their economic exploitation strategies (Amazonia, Peru) (Van Valkenburg et al., 2020). This work analyses one of the most important archaeological areas in central Ecuador, Cerros de Hojas-Jaboncillo (Figs. 1 & 2), which has traditionally been identified as a major centre of the Manteña culture. This culture spanned the current provinces of Manabí, Santa Elena and Guayas between the 8<sup>th</sup> and 15<sup>th</sup> centuries AD.</p><p class="VARAbstract">Although the site was discovered in the early 20th century (Saville, 1907 & 1910; Estrada, 1957) on the slopes and summits of the Chongón-Colonche and Balzar hills range (formerly called Sierra de Bálsamo), it has not been until recent moments that the characterization of its extension, its spatial conformation, as well as the main geoarchaeological characteristics of the site have begun to be possible (López, 2008; Delgado, 2009; Tobar, 2012; Lunniss, 2012; Veintimilla-Bustamante, 2012). The limitations of previous studies were largely due to the prevalence of thick sub-tropical forests, whose trees are sometimes 14 m tall.</p><p class="VARNormal">In 2016, a large-scale LiDAR flight covering 147.78 km² at a density of 9 ppm² has revealed the main features of the archaeological complex, including various sites that illustrate a multi-nuclear settlement pattern (Figs. 5, 8 & 10). The study suggests that settlements, between 0.5 and 3 ha in size (Fig. 8), followed a common pattern, standing on small long hilltop plateaus. The complexes were visually connected and were linked by a road network.</p><p class="VARNormal">There is little doubt that one of the most important discoveries concerns the relationship between archaeological sites and complex agrarian systems, which point to the exploitation of wide areas. These had irrigation systems that involved rainwater retention and water resources maximisation (Figs. 6 & 8). The system made intensive rainwater use during the humid season (December-May); it also took advantage of the coastal mists that ensure precipitations on the hilltops.</p><p class="VARNormal">At the same time, the LiDAR survey has established differences between the settlement pattern in the north-eastern sector, in which the interaction between settlements and the agro-system is not so obvious, and the south-western sector, whose settlements are clearly related to wide terraced systems.</p><p class="VARNormal">These results will allow for comparisons between Cerros de Hojas-Jaboncillo and other known Manteño sites, such as Japotó (Bouchard, 2010), Agua Blanca (McEwan & Silva, 1992) and Ligüiqui. All of them point to the continued occupation of the same areas, and the continuity of some sites at least between the Guangala period (500 BC.-600 AD) and the late Manteño period (12<sup>th</sup>-15<sup>th</sup> centuries), as seen in Salango and its territory (Béarez, Gay & Lunniss, 2012).</p><p class="VARNormal">Most research in the 20<sup>th</sup> century has argued for an increase in social complexity during the classic Manteño period (8<sup>th</sup>-11<sup>th</sup> centuries), following a diffusionist explanatory pattern (Estrada, 1957). However, the LiDAR analysis of the Cerros de Hojas-Jaboncillo site points to prolonged periods of stable management of the environment, a response to the need to make use of the available resources. Archaeology, through large-scale landscape studies and the in-depth characterisation of sites through wide-ranging sampling methodologies can contribute to answering the question as to whether environmental management led to the formation of a “Manteño state” or whether this society can be better defined as a chiefdom (Touchard-Houlbert, 2010).</p><p class="VARNormal"><strong>Highlights:</strong></p><ul><li><p>The study focused on the LiDAR research of the Cerro de Hojas-Jaboncillo, the most important pre-Hispanic site in the Manabi province (Ecuador).</p></li><li><p>LiDAR technology has allowed users to identify an intense occupation in a forest area, where until now, it was impossible to detect archaeological structures.</p></li><li><p>The study has established the close relationship between the pre-Hispanic occupation model and the use of complex irrigation systems.</p></li></ul>

GIS Analysis and Spatial Networking Patterns in Upland Ancient Warfare: The Roman Conquest of Dacia

Generally seen as natural peripheries, upland landscapes present particular challenges both in terms of living, and of recording past human activity within. LiDAR (light detection and ranging) technology has now brought considerable improvement in our ability to record and map surviving archaeological features, but not necessarily increased our appreciation of local agency. Though the iconic landscape around the Iron Age Dacian capital of Sarmizegetusa Regia (Romania) and its Roman conquest have long caught the attention of specialists and the wider public, both previous research and more recent results from an airborne LiDAR survey leave considerable gaps in our understanding of networking potential across this challenging landscape. Based on LiDAR and satellite-generated high- and mid-resolution topographic data, our paper employs an innovative combination of GIS (geographic information system) spatial analysis tools to examine the spatial relationships between Roman military bases, Dacian targets, and the wider landscape as an integral part of a wider interdisciplinary archaeological research. This helped us formulate and test spatial and historical hypotheses, according to which all known and potential Roman military bases in the study area functioned as part of a system where each contributed individual advantages in securing their domination across the landscape. Our research highlighted the advantages and challenges for Comărnicelu as one of the key Roman logistical nodes, and for the attackers at Şesului and Muncelu working in tandem to besiege and subdue Sarmizegetusa Regia. Our study raises doubts with respect of the fall and destruction of the hillfort at Vârfu lui Hulpe as a result of a Roman siege, making space for alternative political narratives. Ultimately, our findings help build a better understanding of this iconic world heritage landscape and its Roman conquest.

A risk analysis framework for transmission towers under potential pluvial flood - LiDAR survey and geometric modelling

The failure of transmission towers often causes widespread blackouts and results in electrical shock to people and animals via water bodies especially in areas prone to river floods and storm surges. To ensure safety operation of the power networks, the structural health of transmission towers should be maintained and investigated under the potential of flood hazards. We propose a comprehensive risk assessment framework for the transmission towers affected by the pluvial flood, based on the geometry of the tower and the terrain. The geometry is first obtained from 3D point clouds derived from airborne Light Detection And Ranging (LiDAR). The framework consists of a series of new point cloud segmentation and fitting algorithms, designed to accurately estimate the inclination angles of the transmission towers. In addition, the framework includes an existing flood risk index (Topographic Control Index, TCI) and a new risk index for the transmission towers (Transmission Tower Risk Index, TTRI) which reflects the risk for tower damages under the pluvial flooding. The framework is applied to Hengman, situated on the Pearl River estuary, Southern China. Based on our results, high TTRI were shown to be often associated with high flooding risk. This coincides with our hypothesis that the floods weaken foundation settlements and thus trigger tower inclinations. The TTRI and TCI analyses reveal that 8 towers out of ~60 in total are with low-risk (13.8%); 2 with medium-risk (3.5%) and 1 tower is at high-risk. The total area affected by the risk tower is ~2.7 km2, accounting for 5.5% of the study area. The results suggested that the proposed framework is implementable and reliable. The resultant analysis can help decision makers to pinpoint the affected transmission towers along with the peripheral affected areas for precaution.

Findings from a Combined Subsea LiDAR and Multibeam Survey at Kingston Reef, Western Australia

Light Detection and Ranging (LiDAR), a comparatively new technology in the field of underwater surveying, has principally been used for taking precise measurement of undersea structures in the oil and gas industry. Typically, the LiDAR is deployed on a remotely operated vehicle (ROV), which will “land” on the seafloor in order to generate a 3D point cloud of its environment from a stationary position. To explore the potential of subsea LiDAR on a moving platform in an environmental context, we deployed an underwater LiDAR system simultaneously with a multibeam echosounder (MBES), surveying Kingston Reef off the coast of Rottnest Island, Western Australia. This paper compares and summarises the relative accuracy and characteristics of underwater LiDAR and multibeam sonar and investigates synergies between sonar and LiDAR technology for the purpose of benthic habitat mapping and underwater simultaneous localisation and mapping (SLAM) for Autonomous Underwater Vehicles (AUVs). We found that LiDAR reflectivity and multibeam backscatter are complementary technologies for habitat mapping, which can combine to discriminate between habitats that could not be mapped with either one alone. For robot navigation, SLAM can be effectively applied with either technology, however, when a Global Navigation Satellite System (GNSS) is available, SLAM does not significantly improve the self-consistency of multibeam data, but it does for LiDAR.

Tidal Creek Extraction from Airborne LiDAR Data Using Ground Filtering Techniques

As elongated indentations or valleys in a wetland caused by tidal currents, tidal creeks act as drainage pathways and promote tidal flat evolution. Determining their geometric information is essential for topographical research of tidal flats. The airborne light detection and ranging (LiDAR) system has been the most efficient surveying technique in tidal topography because it can directly acquire precise geo-referenced point clouds for wide areas. Existing tidal creek extraction methods using airborne LiDAR data have limitations such as excessive user intervention, lack of adaptability to various shapes and sizes of tidal creeks, and decreased precision due to conversion to the digital elevation model. This study aims to overcome these limitations and effectively extract various types of tidal creeks by utilizing ground filtering which is a technique to filter off-ground objects (such as buildings, trees, etc.) in land LiDAR surveys. To derive a suitable method for tidal creek extraction, three verified ground filtering techniques, adaptive triangulated irregular network, gLiDAR, and cloth simulation filtering (CSF), were selected and tested using LiDAR point data. We modified the application procedure and optimized their parameters to enable tidal creek extraction. Our results confirmed that CSF can extract various tidal creeks with minimal user intervention. Finally, we calculated their depths and generated a tidal creek map.

Improving the Accuracy of Automatic Reconstruction of 3D Complex Buildings Models from Airborne Lidar Point Clouds

Due to high requirements of variety of 3D spatial data applications with respect to data amount and quality, automatized, efficient and reliable data acquisition and preprocessing methods are needed. The use of photogrammetry techniques—as well as the light detection and ranging (LiDAR) automatic scanners—are among attractive solutions. However, measurement data are in the form of unorganized point clouds, usually requiring transformation to higher order 3D models based on polygons or polyhedral surfaces, which is not a trivial process. The study presents a newly developed algorithm for correcting 3D point cloud data from airborne LiDAR surveys of regular 3D buildings. The proposed approach assumes the application of a sequence of operations resulting in 3D rasterization, i.e., creation and processing of a 3D regular grid representation of an object, prior to applying a regular Poisson surface reconstruction method. In order to verify the accuracy and quality of reconstructed objects for quantitative comparison with the obtained 3D models, high-quality ground truth models were used in the form of the meshes constructed from photogrammetric measurements and manually made using buildings architectural plans. The presented results show that applying the proposed algorithm positively influences the quality of the results and can be used in combination with existing surface reconstruction methods in order to generate more detailed 3D models from LiDAR scanning.

Comparing Individual Tree Height Information Derived from Field Surveys, LiDAR and UAV-DAP for High-Value Timber Species in Northern Japan

High-value timber species such as monarch birch (Betula maximowicziana Regel), castor aralia (Kalopanax septemlobus (Thunb.) Koidz), and Japanese oak (Quercus crispula Blume) play important ecological and economic roles in forest management in the cool temperate mixed forests in northern Japan. The accurate measurement of their tree height is necessary for both practical management and scientific reasons such as estimation of biomass and site index. In this study, we investigated the similarity of individual tree heights derived from conventional field survey, digital aerial photographs derived from unmanned aerial vehicle (UAV-DAP) data and light detection and ranging (LiDAR) data. We aimed to assess the applicability of UAV-DAP in obtaining individual tree height information for large-sized high-value broadleaf species. The spatial position, tree height, and diameter at breast height (DBH) were measured in the field for 178 trees of high-value broadleaf species. In addition, we manually derived individual tree height information from UAV-DAP and LiDAR data with the aid of spatial position data and high resolution orthophotographs. Tree heights from three different sources were cross-compared statistically through paired sample t-test, correlation coefficient, and height-diameter model. We found that UAV-DAP derived tree heights were highly correlated with LiDAR tree height and field measured tree height. The performance of individual tree height measurement using traditional field survey is likely to be influenced by individual species. Overall mean height difference between LiDAR and UAV-DAP derived tree height indicates that UAV-DAP could underestimate individual tree height for target high-value timber species. The height-diameter models revealed that tree height derived from LiDAR and UAV-DAP could be better explained by DBH with lower prediction errors than field measured tree height. We confirmed the applicability of UAV-DAP data for obtaining the individual tree height of large-size high-value broadleaf species with comparable accuracy to LiDAR and field survey. The result of this study will be useful for the species-specific forest management of economically high-value timber species.

DepthLearn: Learning to Correct the Refraction on Point Clouds Derived from Aerial Imagery for Accurate Dense Shallow Water Bathymetry Based on SVMs-Fusion with LiDAR Point Clouds

The determination of accurate bathymetric information is a key element for near offshore activities; hydrological studies, such as coastal engineering applications, sedimentary processes, hydrographic surveying, archaeological mapping and biological research. Through structure from motion (SfM) and multi-view-stereo (MVS) techniques, aerial imagery can provide a low-cost alternative compared to bathymetric LiDAR (Light Detection and Ranging) surveys, as it offers additional important visual information and higher spatial resolution. Nevertheless, water refraction poses significant challenges on depth determination. Till now, this problem has been addressed through customized image-based refraction correction algorithms or by modifying the collinearity equation. In this article, in order to overcome the water refraction errors in a massive and accurate way, we employ machine learning tools, which are able to learn the systematic underestimation of the estimated depths. In particular, an SVR (support vector regression) model was developed, based on known depth observations from bathymetric LiDAR surveys, which is able to accurately recover bathymetry from point clouds derived from SfM-MVS procedures. Experimental results and validation were based on datasets derived from different test-sites, and demonstrated the high potential of our approach. Moreover, we exploited the fusion of LiDAR and image-based point clouds towards addressing challenges of both modalities in problematic areas.