Using the Total Apparent Spacing Approach to Optimize Foundation Drain Orientations

Forest canopy openings are a key element of forest structure, influencing a host of ecological dynamics. Light detection and ranging (LiDAR) is the de-facto standard for measuring three-dimensional forest structure, but digital aerial photogrammetry (DAP) has emerged as a viable and economical alternative. We compared the performance of LiDAR and DAP data for characterizing canopy openings and no-openings across a 1-km2 expanse of boreal forest in northern Alberta, Canada. Structural openings in canopy cover were delineated using three canopy height model (CHM) alternatives, from (i) LiDAR, (ii) DAP, and (iii) a LiDAR/DAP hybrid. From a point-based detectability perspective, the LiDAR CHM produced the best results (87% overall accuracy), followed by the hybrid and DAP models (47% and 46%, respectively). The hybrid and DAP CHMs experienced large errors of omission (9–53%), particularly with small openings up to 20m2, which are an important element of boreal forest structure. By missing these, DAP and hybrid datasets substantially under-reported the total area of openings across our site (152,470 m2 and 159,848 m2, respectively) compared to LiDAR (245,920 m2). Our results illustrate DAP’s sensitivity to occlusions, mismatched tie points, and other optical challenges inherent to using structure-from-motion workflows in complex forest scenes. These under-documented constraints currently limit the technology’s capacity to fully characterize canopy structure. For now, we recommend that operational use of DAP in forests be limited to mapping large canopy openings, and area-based attributes that are well-documented in the literature.

The detailed representation of the topography is an important aspect when dealing with any flood simulation model. A Digital Surface Model (DSM) with fine spatial resolution can capture topographic object boundaries with a high level of detail. Light detection and ranging (LiDAR) is a technology that can rapidly collect elevation data of a relatively large topographic area with fine spatial resolution (< 0.5m ∼1m). Information about physical objects on the DSM is critical for storm water management as the propagation of flood water is influenced by both the terrain and the objects on the terrain. The processing of LiDAR data is an important task for generating an accurate DSM suitable to running a flood simulation model. Filtering is the first step in processing LiDAR data. In the LiDAR community, the term “filtering” means the task of separating LiDAR data into ground (bare earth) and non-ground points (buildings and trees). However, the choice of appropriate filtering algorithm is crucial in order to detect micro objects such as boundary walls, fences, and thin embankments that have significant impact on the surface flow pattern. This study offers an innovative approach for developing a LiDAR filtering algorithm that is flexible in nature and able to detect not only buildings and trees but also the micro objects. The filtering algorithm is applied to two study areas, and the output suggests close correspondence to reality.

Civil engineering includes a wide range of applications that require three-dimensional (3D) information. With the advent of LiDAR (Light Detection And Ranging) technology, obtaining 3D terrain information accurately in a cost and time effective manner became more popular. In this paper, three case studies demonstrate the roles and applications of airborne LiDAR data including highway, transmission line and hydropower dam development projects. In first case study, the highway alignment was considerably changed after the initial design stage. LiDAR data was able to do the re-alignment survey quickly, allowing for prompt changes in design. In the next case study, a comparison is made between conventional transmission route alignment design and the design using PLS-CADD with LiDAR data. Utilizing the LiDAR data in PLSCADD allows transmission line engineers to model the construction costs of several alignments options effectively and efficiently compared to the conventional method. The last case study uses LiDAR for obtaining the necessary information for proposed dam. Using LiDAR data maximizes topological accuracy and volume measurements while minimizing survey time and costs. Furthermore, utilizing the multi-temporal LiDAR data can reveal valuable information such as topography changes. LiDAR survey technology has proven that it is the best method to capture accurate data from the earth surface which provides maximum density of topographical &amp; digital imagery information for various applications.

National and regional data products of the ecosystem structure derived from airborne laser scanning (ALS) surveys with Light Detection And Ranging (LiDAR) technology are essential for ecology, biodiversity, and ecosystem monitoring. However, noises like powerlines often remain, hindering the accurate measurement of 3D ecosystem structures from LiDAR. Currently, there is a lack of studies assessing powerline noise removal in the context of generating data products of ecosystem structures from ALS point clouds. Here, we assessed the (1) performance and accuracy, (2) effectiveness, and (3) time efficiency and execution time of three powerline extraction methods (i.e. two point-based methods based on deep learning and eigenvalue decomposition, respectively, and one hybrid method) for removing powerline noise when quantifying 3D ecosystem structures in landscapes with varying canopy heights and vegetation openness. Twenty-five LiDAR metrics representing three key dimensions of the ecosystem structure (i.e. vegetation height, cover, and vertical variability) across 10 study areas in the Netherlands were used for our assessment. The deep learning method had the best performance and showed the highest accuracy of powerline removal across various landscape types (average F 1 score = 96%), closely followed by the hybrid method (average F 1 score = 95%). In contrast, the accuracy of the eigenvalue decomposition method was lower (average F 1 score = 82%) and depended on landscape context and vegetation composition (e.g. the F 1 score decreased from 96% to 63% when the average canopy height increased across landscapes). Powerline noise removal had the highest effectiveness (i.e. generating LiDAR metrics closest to those derived from manually labeled ground truth data) for LiDAR metrics capturing height and cover of low- and high-vegetation layers. Time efficiency (processed points per second) was highest for the eigenvalue decomposition method, yet the hybrid method reduced the execution time by > 50% compared to the deep learning method (ranging from 20% to 89% in study areas with different landscape composition). Based on our findings, we recommend the hybrid method for upscaling powerline removal on multi-terabyte ALS datasets to a regional or national extent because of its high accuracy and computational efficiency. Remaining misclassifications in LiDAR metrics could be further minimized by improving the training dataset for deep learning models (e.g. including various shapes of transmission towers from different datasets). Our findings provide novel insights into the performance of different powerline extraction methods, how their effectiveness varies for improving vegetation metrics and mapping the 3D ecosystem structure from LiDAR, and their computational efficiency for upscaling powerline removal in multi-terabyte ALS datasets to a national extent.

Chapter 7 - Landslide analysis using laser scanners

A second-generation Gamma Remote Sensing Portable RAdio Detection and Ranging (RADAR) Interferometer (GPRI-II) and a Lieca C-10 LIght Detection and Ranging (LIDAR) were employed to monitor the volume change behavior of two thin compacted clay test sections at the University of Arkansas Cato Springs Research Center (UACRSC). The viability of using remote sensing methods to accurately identify and quantify deformation associated with expansive clay behavior was evaluated. Two 464.52 m2 (5,000 ft2) sections consisting of 20.3 cm (8 inches) thick compacted clay layer underlain by a 5.1 cm (2 inches) thick sand drainage blanket were constructed. One test section was amended with three (Phase 1) and six (Phase 2) percent sodium bentonite. Four LIDAR and 355 RADAR observations were captured from the roof of the UACSRC building between January and July, 2012. Multiple RADAR images were captured on a weekly basis and after significant precipitation events. RADAR measurements, obtained by calculating the interferometric phase difference between coregistered temporally separated RADAR images, were used to determine topography and deformation. LIDAR measurements, as obtained from LIDAR point clouds, were also used to determine topography and deformation. To obtain accurate topography and deformation measurements the vegetation effects within the LIDAR data was removed. Deformation maps generated using both the RADAR and LIDAR methods are presented and compared. The results indicate both technologies are capable of detecting surface movements associated with expansive clay behavior. Moreover, the surface movements associated with expansive clay behavior can be accurately quantified using the LIDAR device.

LiDAR (Light Detection and Ranging) is a positive remote sensing technology. In this paper, with analyzed the characteristic of LiDAR data and discussed the advantages of LiDAR data, we present an improved data filtering algorithm for LiDAR data. It is basing on the last return laser pulses and tries to design an fast pre-processing LiDAR point data with the purpose of DEM Modeling from LiDAR based point cloud data. The algorithm contains two times of filtering with different aims and data process strategies. Experiments of real terrain area are presented for testing efficiency and accuracy of this filtering method.

Routine bridge inspections require labor intensive and highly subjective visual interpretation to determine bridge deck surface condition. Light Detection and Ranging (LiDAR) a relatively new class of survey instrument has become a popular and increasingly used technology for providing as-built and inventory data in civil applications. While an increasing number of private and governmental agencies possess terrestrial and mobile LiDAR systems, an understanding of the technology’s capabilities and potential applications continues to evolve. LiDAR is a line-of-sight instrument and as such, care must be taken when establishing scan locations and resolution to allow the capture of data at an adequate resolution for defining features that contribute to the analysis of bridge deck surface condition. Information such as the location, area, and volume of spalling on deck surfaces, undersides, and support columns can be derived from properly collected LiDAR point clouds. The LiDAR point clouds contain information that can provide quantitative surface condition information, resulting in more accurate structural health monitoring. LiDAR scans were collected at three study bridges, each of which displayed a varying degree of degradation. A variety of commercially available analysis tools and an independently developed algorithm written in ArcGIS Python (ArcPy) were used to locate and quantify surface defects such as location, volume, and area of spalls. The results were visual and numerically displayed in a user-friendly web-based decision support tool integrating prior bridge condition metrics for comparison. LiDAR data processing procedures along with strengths and limitations of point clouds for defining features useful for assessing bridge deck condition are discussed. Point cloud density and incidence angle are two attributes that must be managed carefully to ensure data collected are of high quality and useful for bridge condition evaluation. When collected properly to ensure effective evaluation of bridge surface condition, LiDAR data can be analyzed to provide a useful data set from which to derive bridge deck condition information.

The continuous and reliable operation of subsea pipelines is of critical importance to the global oil and gas industry, as these pipelines are responsible for transporting vast quantities of hydrocarbons from offshore production fields to onshore facilities. However, the harsh underwater environment in which these pipelines operate presents numerous challenges, including exposure to corrosive seawater, extreme pressure variations, and mechanical stress from geological movements. These factors can cause significant structural degradation over time, leading to potential corrosion, material fatigue, and deformation. Without regular monitoring, such issues may go undetected, increasing the risk of leaks, spills, or catastrophic failures, which would have severe environmental, economic, and safety consequences. Traditionally, subsea pipeline monitoring has relied on periodic inspections using technologies such as remotely operated vehicles (ROVs), sonar, and visual assessments. While these methods have proven useful, they often suffer from limitations such as low resolution, infrequent data collection, and the high operational costs associated with deploying ROVs and divers for manual inspections. Moreover, these traditional methods may not be capable of providing the continuous, real-time data needed to make proactive maintenance decisions and ensure pipeline integrity. This research paper focuses on the application of LiDAR (Light Detection and Ranging) technology as a transformative solution for monitoring the structural health of subsea pipelines. LiDAR is a remote sensing technology that uses laser pulses to capture detailed, high-resolution 3D data about surfaces and environments. By adapting LiDAR for underwater use, this paper proposes a novel method for real-time, continuous monitoring of pipeline conditions. The use of LiDAR in subsea environments presents unique opportunities to detect early signs of corrosion, material degradation, and structural deformation with a level of precision that is difficult to achieve with conventional monitoring techniques. The key advantage of LiDAR technology lies in its ability to collect accurate and comprehensive data in challenging conditions. Underwater LiDAR systems can penetrate turbid water, providing clear images and measurements of pipeline surfaces, even in low-visibility environments. The resulting high-resolution 3D models allow for the detailed analysis of pipeline integrity, enabling operators to track changes in pipeline conditions over time and respond to potential issues before they escalate. This capability is particularly important for detecting corrosion and deformation, which can occur gradually and may not be immediately apparent using traditional methods.In this paper, we explore the current state of subsea pipeline monitoring technologies, comparing the strengths and limitations of existing approaches. We then introduce a LiDAR-based solution, discussing the technical adaptations necessary for using LiDAR in subsea conditions. This includes considerations for data acquisition, processing, and analysis, as well as the challenges posed by underwater environments, such as light absorption and scattering by water particles. Furthermore, mathematical models for calculating corrosion rates and predicting deformation based on LiDAR data will be presented. These models allow for quantitative assessments of the structural health of pipelines, enabling operators to make informed decisions about maintenance schedules and interventions. The use of real-time data not only improves the accuracy of these models but also provides the ability to forecast future pipeline behavior, reducing the likelihood of unexpected failures. This paper also includes case studies and diagrams to demonstrate the practical application of LiDAR in subsea pipeline monitoring. By presenting real-world examples of how this technology has been implemented, we aim to highlight its effectiveness in improving the safety and longevity of critical infrastructure in the oil and gas sector. LiDAR technology represents a significant advancement in the field of subsea pipeline monitoring, offering enhanced data accuracy, real-time monitoring capabilities, and cost-effective solutions for ensuring pipeline integrity. This research paper not only outlines the potential of LiDAR as a monitoring tool but also provides a roadmap for its implementation in the oil and gas industry, with the ultimate goal of reducing risks, minimizing environmental impacts, and ensuring the continued safe operation of subsea pipelines.

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Light Detection and Ranging (LiDAR) is a promising type of sensor for autonomous driving that utilizes laser technology to provide perceptions and accurate distance measurements of obstacles in the vehicle path. In recent years, there has also been a rise in the implementation of LiDARs in modern and autonomous vehicles to aid self-driving features. However, navigating adverse weather remains one of the biggest challenges in achieving Level 5 full autonomy due to sensor soiling, leading to performance degradation that can pose safety hazards. When driving in rain, raindrops impact the LiDAR sensor assembly and cause attenuation of signals when the light beams undergo reflections and refractions. Consequently, signal detectability, accuracy, and intensity are significantly affected. To date, limited studies have been able to perform objective evaluations of LiDAR performance, most of which faced limitations that hindered realistic, controllable, and repeatable testing. Therefore, this paper reports a fundamental study that employs a previously developed novel wind tunnel testing methodology to investigate and quantify the effects of stress factors affecting LiDAR perception. It was found that soiling characteristics, such as raindrop size distribution and droplet impact kinematics, and material properties, such as surface roughness and wettability, all play critical roles in influencing LiDAR performance to different extents. The results suggest that although LiDAR is an optical type of sensor, its perception does not necessarily align with camera vision, which is closer to human perception. Specifically, hydrophilic surfaces show better advantages over hydrophobic surfaces for LiDAR sensor applications when driving in rain. The overall relationships between material properties, adherent raindrop dynamics, and LiDAR perceptions are summarized.&lt;/div&gt;&lt;/div&gt;

Background The creation of defensible space, an area around the house where fuel is managed according to specific guidelines, can reduce ignition and fire spread, mitigating wildfire threat to communities. Remote sensing approaches can facilitate assessments of defensible spaces. Aims To develop a protocol to evaluate which defensible space hazards can be accurately assessed using Light Detection and Ranging (LiDAR). Methods We used high and low point density airborne LiDAR to measure hazards in 31 gardens in southeast Tasmania (Australia) based on alignment with defensible space guidelines. We compared the resulting ‘garden hazard scores’ with those generated from field observations to identify garden characteristics that can be estimated using LiDAR, as well as the best analysis resolution to do so. Key results We found a good association between field data and LiDAR measures of vegetation cover and spatial arrangement, but not plant flammability, inferred using Normalised Difference Vegetation Index (NDVI), or flammable objects. Low-density LiDAR consistently underestimated cover metrics compared with High-density LiDAR. Nonetheless, garden hazard scores calculated using either high- or low-density LiDAR yielded results similar to those obtained using only field observations. Implications LiDAR can be used to conduct semi-automatic, large-scale measurements of some, albeit not all, critical garden hazards.

The combination of unmanned aerial vehicle (UAV) photography with structure-from-motion (SfM) digital photogrammetry provides a quickly deployable and cost-effective method for monitoring geomorphic change, particularly for hazards such as landslides. The Scenic Drive landslide is a deep-seated slope failure in La Honda, CA, with episodic activity in 1998 and 2005–06. Heavy rainfall during 2016–17 initiated movement of a new and separate landslide directly upslope of the existing Scenic Drive landslide, damaging three residences. We acquired imagery of the Upper Scenic Drive landslide beginning 2 days after initial motion using a global positioning system–enabled UAV. We used this imagery to generate seven digital elevation models (DEMs) between January and May 2017, with spatial resolutions of ∼3–10 cm/pixel. We compared these DEMs with each other and with available light detection and ranging (LiDAR) data to assess landslide kinematics, including horizontal displacement vectors, rates of motion, and total mass redistribution, and to test the accuracy and applicability of UAV/SfM-derived measurements. We estimated the maximum horizontal displacement of the slide was at least 5 m during the monitoring period and calculated that ∼3,000 m3 of material was displaced by the landslide. Comparing the UAV-derived topography with synchronous terrestrial LiDAR scanning showed that accuracies of the two techniques are comparable, generally within 0.05 m horizontally and within 0.20 m vertically in unvegetated areas. This study demonstrates the capability of combining UAV and SfM to map and monitor active geomorphic processes in emergent situations where high-resolution digital topography is needed in near-real-time.

This chapter introduces the data processing methods for two typical robotic perception systems: light detection and ranging (LIDAR)-based and Flash LIDAR Camera (FLC)-based systems. The most popular 3D perception sensors in robotics include stereovision system and light detection and ranging (LIDAR). The representative applications of stereovision have been demonstrated in NASA's Mars Exploration Rover [4, 5] program and the DARPA's Learning Applied to Ground Robots [6–9] program. A LIDAR system outperforms a stereovision system in terms of the completeness and accuracy of range data. An FLC illuminates the entire scene with a single laser pulse (or modulated infrared light) and focuses the image onto the 3D sensor's focal plane array (FPA). The sensors used for mapping and navigation include a 2D LIDAR and a three-axis fiber optic gyro. A robotic device is a portable robotic system with navigational functions such as positioning, obstacle detection, and scene recognition.

Data ketinggian Digital Elevation Model (DEM) terdiri atas Digital Surface Model (DSM) dan Digital Terrain Model (DTM). DSM adalah informasi yang menggambarkan model permukaan bumi beserta seluruh objek yang berada di atasnya. Sedangkan DTM menggambarkan model permukaan bumi tanpa objek yang berada di atasnya di wilayah darat. DTM dibentuk dari kelas ground dan dilakukan hydro-flattening yaitu dengan meratakan area permukaan air. dengan kata lain, DTM merupakan kumpulan titik data sampel yang terus mewakili distribusi spasial dari berbagai jenis informasi di medan. Contoh sumber data yang dapat dugunakan dalam pembuatan DTM adalah data LiDAR. DTM kemudian dapat digunakan dalam pembuatan kontur peta. Ketelitian kontur pada peta RBI diatur dalam Peraturan Kepala Badan Informasi Geospasial No. 6 tahun 2018 serta Peraturan Kepala Badan Informasi Geospasial No. 1 Tahun 2020. Data yang digunakan dalam penelitian ini yaitu data LiDAR. Pengolahan data tersebut menggunakan metode yaitu Slope Based Filtering (SBF). Setelah dilakukan proses pengolahan data pada data LiDAR kemudian didapatkan hasil berupa kontur. Dimana Kemudian setelah terbentuk kontur dari masing-masing data, dilakukan pengujian ketelitian berdasarkan PERKA BIG No. 6 Tahun 2018 dan PERKA BIG No. 1 Tahun 2020. Hasil dari penelitian ini yaitu uji LE90 pengolahan data LiDAR sebesar 0.523 m. Berdasarkan Perka BIG No. 6 Tahun 2018 dan PERKA BIG No. 1 Tahun 2020 pada ketelitian peta skala 1:5000, hasil pengolahan data LiDAR masuk kelas 1. Berdasarkan hasil kontur yang dihasilkan, Kontur hasil dari LiDAR memiliki kerapatan yang terbilang baik. Hal ini dikarenakan pada hasil dari LiDAR memiliki nilai RMS Error yang rendah.

Digital Elevation Models (DEMs) contribute to geomorphological and hydrological applications. DEMs can be derived using different remote sensing-based datasets, such as Interferometric Synthetic Aperture Radar (InSAR) (e.g., Advanced Land Observing Satellite (ALOS) Phased Array type L-band SAR (PALSAR) and Shuttle Radar Topography Mission (SRTM) DEMs). In addition, there is also the Digital Surface Model (DSM) derived from optical tri-stereo ALOS Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) imagery. In this study, we evaluated satellite-based DEMs, SRTM (Global) GL1 DEM V003 28.5 m, ALOS DSM 28.5 m, and PALSAR DEMs 12.5 m and 28.5 m, and their derived channel networks/orders. We carried out these assessments using Light Detection and Ranging (LiDAR) Digital Surface Models (DSMs) and Digital Terrain Models (DTMs) and their derived channel networks and Strahler orders as reference datasets at comparable spatial resolutions. We introduced a pixel-based method for the quantitative horizontal evaluation of the channel networks and Strahler orders derived from global DEMs utilizing confusion matrices at different flow accumulation area thresholds (ATs) and pixel buffer tolerance values (PBTVs) in both ±X and ±Y directions. A new Python toolbox for ArcGIS was developed to automate the introduced method. A set of evaluation metrics—(i) producer accuracy (PA), (ii) user accuracy (UA), (iii) F-score (F), and (iv) Cohen’s kappa index (KI)—were computed to evaluate the accuracy of the horizontal matching between channel networks/orders extracted from global DEMs and those derived from LiDAR DTMs and DSMs. PALSAR DEM 12.5 m ranked first among the other global DEMs with the lowest root mean square error (RMSE) and mean difference (MD) values of 4.57 m and 0.78 m, respectively, when compared to the LiDAR DTM 12.5 m. The ALOS DSM 28.5 m had the highest vertical accuracy with the lowest recorded RMSE and MD values of 4.01 m and −0.29 m, respectively, when compared to the LiDAR DSM 28.5 m. PALSAR DEM 12.5 m and ALOS DSM 28.5 m-derived channel networks/orders yielded the highest horizontal accuracy when compared to those delineated from LiDAR DTM 12.5 m and LiDAR DSM 28.5 m, respectively. The number of unmatched channels decreased when the PBTV increased from 0 to 3 pixels using different ATs.