Model minimum performance specifications for lidar (laser) speed measurement devices.

Mode of access: Internet.

Evaluation of Potential Post-Glacial Faults Using Bare-Earth LiDAR Topographic Data Near Ridley Island, British Columbia

I present results of my evaluation to identify topographic lineaments that are potentially related to post-glacial faulting using bare-earth LiDAR topographic data near Ridley Island, British Columbia. The purpose of this evaluation has been to review bare-earth LiDAR data for evidence of post-glacial faulting in the area surrounding Ridley Island and provide a map of the potential faults to review and possibly field check. My work consisted of an extensive literature review to understand the tectonic, geologic, glacial and sea level history of the area and analysis of bare-earth LiDAR data for Ridley Island and the surrounding region. Ridley Island and the surrounding north coast of British Columbia have a long and complex tectonic and geologic history. The north coast of British Columbia consists of a series of accreted terranes and some post-accretionary deposits. The accreted terranes were attached to the North American continent during subduction of the Pacific Plate between approximately 200 Ma and 10 Ma. The terrane and post-accretionary deposits are metamorphosed sedimentary, volcanic and intrusive rocks. The rocks have experienced significant deformation and been intruded by plutonic bodies. Approximately 10 Ma subduction of the Pacific Plate beneath the North America Plate ceased along the central and north coast of British Columbia and the Queen Charlotte Fault Zone was formed. The Queen Charlotte Fault Zone is a transform-type fault that separates the Pacific Plate from the North America Plate. Within the past 1 million years, the area has experienced multiple glacial/interglacial cycles. The most recent glacial cycle occurred approximately 23,000 to 13,500 years ago. Few Quaternary deposits have been mapped in the area. The vast majority of seismicity around the northwest coast of British Columbia occurs along the Queen Charlotte Fault Zone. Numerous faults have been mapped in the area, but there is currently no evidence to suggest these faults are active (i.e. have evidence for post-glacial surface displacement or deformation). No earthquakes have been recorded within 50 km of Ridley Island. Several small earthquakes (less than magnitude 6) have been recorded within 100 km of the island. These earthquakes have not been correlated to active faults. GPS data suggests there is ongoing strain in the vicinity of Ridley Island. The strain has the potential to be released along faults, but the calculated strain may be a result of erroneous data or accommodated aseismically. Currently, the greatest known seismic hazard to Ridley Island is the Queen Charlotte Fault Zone. LiDAR data for Ridley Island, Digby Island, Lelu Island and portions of Kaien Island, Smith Island and the British Columbia mainland were reviewed and analyzed for evidence of postglacial faulting. The data showed a strong fabric across the landscape with a northwest-southeast trend that appears to mirror the observed foliation in the area. A total of 80 potential post-glacial faults were identified. Three lineaments are categorized as high, forty-one lineaments are categorized as medium and thirty-six lineaments are categorized as low. The identified features should be examined in the field to further assess potential activity. My analysis did not include areas outside of the LiDAR coverage; however faulting may be present there. LiDAR data analysis is only useful for detecting faults with surficial expressions. Faulting without obvious surficial expressions may be present in the study area.

Using Terrestrial LiDAR to Monitor Erosion within the Gold Basin Landslide Complex, Verlot, WA

In 2014 the United States Forest Service closed the Gold Basin Campground of western Washington in an effort to protect the public from unstable hillslopes directly adjacent to the campground. The Gold Basin Landslide Complex (GBLC) is actively eroding via block fall, dry ravel, and debris flows, which contribute sediment into the South Fork of the Stillaguamish River. This sediment diminishes the salmonid population within the South Fork of the Stillaguamish River by reducing habitable spawning grounds, which is a big concern to the Stillaguamish Tribe of Indians. In this investigation, I quantified patterns of degradation and total volume of sediment erosion from the middle lobe of the GBLC over the period of July 2015 through January 2016 using terrestrial (ground-based) LiDAR (TLS). I characterized site specific stratigraphy and geomorphic processes, and laid the groundwork for future, long-term monitoring of this site. Results of this investigation determined that ~ 4,800m3 of sediment was eroded from the middle lobe of the GBLC during the 6 month study period (July 2015 – January 2016). This erosion likely occurred from debris flows, raveling of poorly sorted sand and gravel deposits and block failures of high plasticity silts and clays, and/or other mass wasting mechanisms. The generalized stratigraphic sequence in the GBLC consists of alternating massive beds of sand and gravel with silts and clays. The low permeability of these silts and clays provide a perfect venue for groundwater to percolate, as I observed during field investigations, which likely contributes to the active instability of the hillslopes. Continued monitoring and mapping of this complex will lead to viable information that could help both the United States Forest Service and the Stillaguamish Tribe.

Evaluation of a method for fusing LIDAR data and multispectral images for building detection

In this paper, we describe the evaluation of a method for building detection by the Dempster-Shafer fusion of LIDAR data and multispectral images. For that purpose, ground truth was digitised for two test sites with quite different characteristics. Using these data sets, the heuristic model for the probability mass assignments of the method is validated, and rules for the tuning of the parameters of this model are discussed. Further we evaluate the contributions of the individual cues used in the classification process to the quality of the classification results. Our results show the degree to which the overall correctness of the results can be improved by fusing LIDAR data with multispectral images.

A generalized adaptive mathematical morphological filter for LIDAR data

Airborne Light Detection and Ranging (LIDAR) technology has become the primary method to derive high-resolution Digital Terrain Models (DTMs), which are essential for studying Earth's surface processes, such as flooding and landslides. The critical step in generating a DTM is to separate ground and non-ground measurements in a voluminous point LIDAR dataset, using a filter, because the DTM is created by interpolating ground points. As one of widely used filtering methods, the progressive morphological (PM) filter has the advantages of classifying the LIDAR data at the point level, a linear computational complexity, and preserving the geometric shapes of terrain features. The filter works well in an urban setting with a gentle slope and a mixture of vegetation and buildings. However, the PM filter often removes ground measurements incorrectly at the topographic high area, along with large sizes of non-ground objects, because it uses a constant threshold slope, resulting in "cut-off" errors. A novel cluster analysis method was developed in this study and incorporated into the PM filter to prevent the removal of the ground measurements at topographic highs. Furthermore, to obtain the optimal filtering results for an area with undulating terrain, a trend analysis method was developed to adaptively estimate the slope-related thresholds of the PM filter based on changes of topographic slopes and the characteristics of non-terrain objects. The comparison of the PM and generalized adaptive PM (GAPM) filters for selected study areas indicates that the GAPM filter preserves the most "cut-off" points removed incorrectly by the PM filter. The application of the GAPM filter to seven ISPRS benchmark datasets shows that the GAPM filter reduces the filtering error by 20% on average, compared with the method used by the popular commercial software TerraScan. The combination of the cluster method, adaptive trend analysis, and the PM filter allows users without much experience in processing LIDAR data to effectively and efficiently identify ground measurements for the complex terrains in a large LIDAR data set. The GAPM filter is highly automatic and requires little human input. Therefore, it can significantly reduce the effort of manually processing voluminous LIDAR measurements.

The 2008 Terrestrial Vegetation of Biscayne National Parko FL, USA Derived From Aerial Photography, NDVI, and LiDAR

Established as a National Park in 1980, Biscayne National Park (BISC) comprises an area of nearly 700 km2 , of which most is under water. The terrestrial portions of BISC include a coastal strip on the south Florida mainland and a set of Key Largo limestone barrier islands which parallel the mainland several kilometers offshore and define the eastern rim of Biscayne Bay. The upland vegetation component of BISC is embedded within an extensive coastal wetland network, including an archipelago of 42 mangrove-dominated islands with extensive areas of tropical hardwood forests or hammocks. Several databases and vegetation maps describe these terrestrial communities. However, these sources are, for the most part, outdated, incomplete, incompatible, or/and inaccurate. For example, the current, Welch et al. (1999), vegetation map of BISC is nearly 10 years old and represents the conditions of Biscayne National Park shortly after Hurricane Andrew (August 24, 1992). As a result, a new terrestrial vegetation map was commissioned by The National Park Service Inventory and Monitoring Program South Florida / Caribbean Network.

Geophysical Analysis of a Central Florida Karst Terrain using Light Detection and Ranging (LIDAR) and Ground Penetrating Radar (GPR) Derived Surfaces

Airborne LIDAR (Light Detecting and Ranging) is a relatively new technique that rapidly and accurately measures micro-topographic features. This study compares topography derived from LIDAR with subsurface karst structures mapped in 3-dimensions with ground penetrating radar (GPR). Over 500 km of LIDAR data were collected in 1995 by the NASA ATM instrument. The LIDAR data was processed and analyzed to identify closed depressions. A GPR survey was then conducted at a 200 by 600 m site to determine if the target features are associated with buried karst structures. The GPR survey resolved two major depressions in the top of a clay rich layer at ~10m depth. These features are interpreted as buried dolines and are associated spatially with subtle (< 1m) trough-like depressions in the topography resolved from the LIDAR data. This suggests that airborne LIDAR may be a useful tool for indirectly detecting subsurface features associated with sinkhole hazard.

An automatic method of above-water iceberg 3-D profiling based on autonomous surface craft (ASC) using surface images and LIDAR

This thesis reports on a novel method to build a 3-D model of the above-water portion of icebergs using surface imaging. The goal is to work towards the automation of iceberg surveys, allowing an Autonomous Surface Craft (ASC) to acquire shape and size information. After collecting data and images, the core software algorithm is made up of three parts: occluding contour finding, volume intersection, and parameter estimation. A software module is designed that could be used on the ASC to perform automatic and fast processing of above-water surface image data to determine iceberg shape and size measurement and determination. The resolution of the method is calculated using data from the iceberg database of the Program of Energy Research and Development (PERD). The method was investigated using data from field trials conducted through the summer of 2014 by surveying 8 icebergs during 3 expeditions. The results were analyzed to determine iceberg characteristics. Limitations of this method are addressed including its accuracy. Surface imaging system and LIDAR system are developed to profile the above-water iceberg in 2015.

Stress Inversion Using LiDAR Tunnel Deformation Measurements

Far-field stresses are those present in a volume of rock prior to excavations being created. Estimates of the orientation and magnitude of far-field stresses, often used in mine design, are generally obtained by single-point measurements of stress, or large-scale, regional trends. Point measurements can be a poor representation of far-field stresses as a result of excavation-induced stresses and geological structures. For these reasons, far-field stress estimates can be associated with high levels of uncertainty. The purpose of this thesis is to investigate the practical feasibility, applications, and limitations of calibrating far-field stress estimates through tunnel deformation measurements captured using LiDAR imaging. A method that estimates the orientation and magnitude of excavation-induced principal stress changes through back-analysis of deformation measurements from LiDAR imaged tunnels was developed and tested using synthetic data. If excavation-induced stress change orientations and magnitudes can be accurately estimated, they can be used in the calibration of far-field stress input to numerical models. LiDAR point clouds have been proven to have a number of underground applications, thus it is desired to explore their use in numerical model calibration. The back-analysis method is founded on the superposition of stresses and requires a two-dimensional numerical model of the deforming tunnel. Principal stress changes of known orientation and magnitude are applied to the model to create calibration curves. Estimation can then be performed by minimizing squared differences between the measured tunnel and sets of calibration curve deformations. In addition to the back-analysis estimation method, a procedure consisting of previously existing techniques to measure tunnel deformation using LiDAR imaging was documented. Under ideal conditions, the back-analysis method estimated principal stress change orientations within ±5° and magnitudes within ±2 MPa. Results were comparable for four different tunnel profile shapes. Preliminary testing using plastic deformation, a rough tunnel profile, and profile occlusions suggests that the method can work under more realistic conditions. The results from this thesis set the groundwork for the continued development of a new, inexpensive, and efficient far-field stress estimate calibration method.