12,784 research outputs found

    Orientation of Airborne Laser Scanning Point Clouds with Multi-View, Multi-Scale Image Blocks

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    Comprehensive 3D modeling of our environment requires integration of terrestrial and airborne data, which is collected, preferably, using laser scanning and photogrammetric methods. However, integration of these multi-source data requires accurate relative orientations. In this article, two methods for solving relative orientation problems are presented. The first method includes registration by minimizing the distances between of an airborne laser point cloud and a 3D model. The 3D model was derived from photogrammetric measurements and terrestrial laser scanning points. The first method was used as a reference and for validation. Having completed registration in the object space, the relative orientation between images and laser point cloud is known. The second method utilizes an interactive orientation method between a multi-scale image block and a laser point cloud. The multi-scale image block includes both aerial and terrestrial images. Experiments with the multi-scale image block revealed that the accuracy of a relative orientation increased when more images were included in the block. The orientations of the first and second methods were compared. The comparison showed that correct rotations were the most difficult to detect accurately by using the interactive method. Because the interactive method forces laser scanning data to fit with the images, inaccurate rotations cause corresponding shifts to image positions. However, in a test case, in which the orientation differences included only shifts, the interactive method could solve the relative orientation of an aerial image and airborne laser scanning data repeatedly within a couple of centimeters

    Detection of Earthflow Using a GPS and LiDAR Integrated Survey: A Case Study from the Slumgullion Landslide, Lake City, Colorado

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    The Slumgullion landslide in the San Juan Mountains near Lake City, Colorado has been a natural laboratory for landslide and environmental studies since the early 1900s. The landslide site covers 4.6 square kilometers and consists of an active part which has been moving continuously for about 300 years over an older, much larger, inactive part. We conducted an integrated GPS and LiDAR survey at the landslide site in one-week period from July 3rd to July 10th, 2015, with the primary purpose of delineating short-term ground deformation associated with the earthflow using advanced GPS and LiDAR techniques. A GPS network with twelve semi-permanent stations was set up, including seven stations on the sliding mass and five stations outside the sliding mass. A RIEGL VZ-2000 terrestrial laser scanner was used to collect data in the field. Airborne laser scanning data were collected by the National Center for Airborne Laser Mapping. We compared different registration methods for datasets acquired by the terrestrial laser scanner. A rapid workflow for field surveying and data processing was developed to generate high-resolution digital terrain models. The movement of the Slumgullion landslide was derived from semi-permanent GPS observations, and two repeated terrestrial laser scanning surveys conducted during the one-week period. A 1.47 cm horizontal daily movement was detected from the GPS observations. We compared different change detection strategies for the LiDAR point clouds measurements. Lateral landslide movements were detected from cloud-to-cloud comparison using the data from terrestrial laser scanning; the accumulated motion ranged from 3 cm to 10 cm during the survey week. The movement measurements derived from GPS and the terrestrial laser scanner agreed well. Our study demonstrates a method of identifying slow earth mass movement using the integration of GPS, terrestrial, and airborne laser scanning datasets. We developed a workflow for terrestrial laser-scanning data processing. Our method could be applied to study landslides in other regions. It is expected that our results will promote the application of GPS and LiDAR techniques in the practice of landslide hazards mitigation.Earth and Atmospheric Sciences, Department o

    VIRTUALIZATION OF FUELBEDS: BUILDING THE NEXT GENERATION OF FUELS DATA FOR MULTIPLE –SCALE FIRE MODELING AND ECOLOGICAL ANALYSIS

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    The primary goal of this research is to advance methods for deriving fine-grained, scalable, wildland fuels attributes in 3-dimensions using terrestrial and airborne laser scanning technology. It is fundamentally a remote sensing research endeavor applied to the problem of fuels characterization. Advancements in laser scanning are beginning to have significant impacts on a range of modeling frameworks in fire research, especially those utilizing 3-dimensional data and benefiting from efficient data scaling. The pairing of laser scanning and fire modeling is enabling advances in understanding how fuels variability modulates fire behavior and effects. This dissertation details the development of methods and techniques to characterize and quantify surface fuelbeds using both terrestrial and airborne laser scanning. The primary study site is Eglin Airforce Base, Florida, USA, which provides a range of fuel types and conditions in a fire-adapted landscape along with the multi-disciplinary expertise, logistical support, and prescribed fire necessary for detailed characterization of fire as a physical process. Chapter 1 provides a research overview and discusses the state of fuels science and the related needs for highly resolved fuels data in the southeastern United States. Chapter 2, describes the use of terrestrial laser scanning for sampling fuels at multiple scales and provides analysis of the spatial accuracy of fuelbed models in 3-D. Chapter 3 describes the development of a voxel-based occupied volume method for predicting fuel mass. Results are used to inform prediction of landscape-scale fuel load using airborne laser scanning metrics as well as to predict post-fire fuel consumption. Chapter 4 introduces a novel fuel simulation approach which produces spatially explicit, statistically-defensible estimates of fuel properties and demonstrates a pathway for resampling observed data. This method also can be directly compared to terrestrial laser scanning data to assess how energy interception of the laser pulse affects characterization of the fuelbed. Chapter 5 discusses the contribution of this work to fire science and describes ongoing and future research derived from this work. Chapters 2 and 4 have been published in International Journal of Wildland Fire and Canadian Journal of Remote Sensing, respectively, and Chapter 3 is in preparation for publication

    Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

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    LiDAR technology is finding uses in the forest sector, not only for surveys in producing forests but also as a tool to gain a deeper understanding of the importance of the three-dimensional component of forest environments. Developments of platforms and sensors in the last decades have highlighted the capacity of this technology to catch relevant details, even at finer scales. This drives its usage towards more ecological topics and applications for forest management. In recent years, nature protection policies have been focusing on deadwood as a key element for the health of forest ecosystems and wide-scale assessments are necessary for the planning process on a landscape scale. Initial studies showed promising results in the identification of bigger deadwood components (e.g., snags, logs, stumps), employing data not specifically collected for the purpose. Nevertheless, many efforts should still be made to transfer the available methodologies to an operational level. Newly available platforms (e.g., Mobile Laser Scanner) and sensors (e.g., Multispectral Laser Scanner) might provide new opportunities for this field of study in the near future

    Ground Profile Recovery from Aerial 3D LiDAR-based Maps

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    The paper presents the study and implementation of the ground detection methodology with filtration and removal of forest points from LiDAR-based 3D point cloud using the Cloth Simulation Filtering (CSF) algorithm. The methodology allows to recover a terrestrial relief and create a landscape map of a forestry region. As the proof-of-concept, we provided the outdoor flight experiment, launching a hexacopter under a mixed forestry region with sharp ground changes nearby Innopolis city (Russia), which demonstrated the encouraging results for both ground detection and methodology robustness.Comment: 8 pages, FRUCT-2019 conferenc

    An approach for real world data modelling with the 3D terrestrial laser scanner for built environment

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    Capturing and modelling 3D information of the built environment is a big challenge. A number of techniques and technologies are now in use. These include EDM, GPS, and photogrammetric application, remote sensing and traditional building surveying applications. However, use of these technologies cannot be practical and efficient in regard to time, cost and accuracy. Furthermore, a multi disciplinary knowledge base, created from the studies and research about the regeneration aspects is fundamental: historical, architectural, archeologically, environmental, social, economic, etc. In order to have an adequate diagnosis of regeneration, it is necessary to describe buildings and surroundings by means of documentation and plans. However, at this point in time the foregoing is considerably far removed from the real situation, since more often than not it is extremely difficult to obtain full documentation and cartography, of an acceptable quality, since the material, constructive pathologies and systems are often insufficient or deficient (flat that simply reflects levels, isolated photographs,..). Sometimes the information in reality exists, but this fact is not known, or it is not easily accessible, leading to the unnecessary duplication of efforts and resources. In this paper, we discussed 3D laser scanning technology, which can acquire high density point data in an accurate, fast way. Besides, the scanner can digitize all the 3D information concerned with a real world object such as buildings, trees and terrain down to millimetre detail Therefore, it can provide benefits for refurbishment process in regeneration in the Built Environment and it can be the potential solution to overcome the challenges above. The paper introduce an approach for scanning buildings, processing the point cloud raw data, and a modelling approach for CAD extraction and building objects classification by a pattern matching approach in IFC (Industry Foundation Classes) format. The approach presented in this paper from an undertaken research can lead to parametric design and Building Information Modelling (BIM) for existing structures. Two case studies are introduced to demonstrate the use of laser scanner technology in the Built Environment. These case studies are the Jactin House Building in East Manchester and the Peel building in the campus of University Salford. Through these case studies, while use of laser scanners are explained, the integration of it with various technologies and systems are also explored for professionals in Built Environmen

    Canopy structural modeling using object-oriented image classification and laser scanning

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    A terrestrial laser scanning (TLS) experiment was carried out in the EAGLE 2006 campaign to characterize and model the canopy structure of the Speulderbos forest. Semi-variogram analysis was used to describe spatial variability of the surface. The dependence of the spatial variability on the applied grid size showed, that in this forest spatial details of the digital surface model are lost in the case of larger than 0.3-0.4 m grid size. Voxel statistics was used for describing the density of the canopy structure. Five zones of the canopy were identified according to their density distribution. Basic geometric structures were tested for modeling the forest at the individual tree level. The results create a firm basis for modeling physical processes in the canopy

    Classification of airborne laser scanning point clouds based on binomial logistic regression analysis

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    This article presents a newly developed procedure for the classification of airborne laser scanning (ALS) point clouds, based on binomial logistic regression analysis. By using a feature space containing a large number of adaptable geometrical parameters, this new procedure can be applied to point clouds covering different types of topography and variable point densities. Besides, the procedure can be adapted to different user requirements. A binomial logistic model is estimated for all a priori defined classes, using a training set of manually classified points. For each point, a value is calculated defining the probability that this point belongs to a certain class. The class with the highest probability will be used for the final point classification. Besides, the use of statistical methods enables a thorough model evaluation by the implementation of well-founded inference criteria. If necessary, the interpretation of these inference analyses also enables the possible definition of more sub-classes. The use of a large number of geometrical parameters is an important advantage of this procedure in comparison with current classification algorithms. It allows more user modifications for the large variety of types of ALS point clouds, while still achieving comparable classification results. It is indeed possible to evaluate parameters as degrees of freedom and remove or add parameters as a function of the type of study area. The performance of this procedure is successfully demonstrated by classifying two different ALS point sets from an urban and a rural area. Moreover, the potential of the proposed classification procedure is explored for terrestrial data

    The use of terrestrial laser scanning for measurements in shallow-water : correction of the 3D coordinates of the point cloud

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    Although acoustic measurements are a wide-spread technique in the field of bathymetry, most systems require a water depth of at least 2 m. Furthermore, mapping shallow-water depths with acoustic techniques is expensive and complicated. Over the last decades, the use of laser scanning for mapping riverbeds has increased. However, the level of accuracy and the point density which can be obtained by Airborne Laser Scanning (ALS), and Airborne Laser Bathymetry (ALB) in particular, are not as high as those of terrain measurements originating from ALS. Moreover, ALS and ALB are not yet suited for mapping shallow-water beds. Therefore, more recent research focuses on the use of Terrestrial Laser Scanning (TLS) from either a fixed or static position (STLS) or from a mobile platform (MTLS). An obvious advantage of using STLS and MTLS is that both the river beds and the river banks can be modelled by means of the same data acquisition system. This ensures a seamless integration of data sets describing both dry and wet surfaces, and thus of topography and bathymetry. However, although STLS and MTLS have the potential to produce high resolution point clouds of shallow-water riverbeds and - banks, the resulting point clouds have to be corrected for the systematic errors in depth and distance that are caused by the refraction of the laser beam at its transition through the boundary of air and water. In this research a procedure was implemented to adjust the coordinates of every point situated beneath the water surface, based on the refractive index. The refractive index depends on the wavelength of the laser beam and the properties of the media the beam travels through. The refractive index for a laser beam with a wavelength of 532 nm varies by less than 1% for a wide range of temperature and salinity conditions. Nevertheless, during the case studies, it became clear that it is important to use an estimate of the refractive index which approaches the actual value as closely as possible in order to obtain accuracies of less than 1 to 2 cm. Therefore, the refractive index was determined for each specific case by using water samples
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