Nesting In The Clouds: Evaluating And Predicting Sea Turtle Nesting Beach Parameters From Lidar Data

Humans' desire for knowledge regarding animal species and their interactions with the natural world have spurred centuries of studies. The relatively new development of remote sensing systems using satellite or aircraft-borne sensors has opened up a wide field of research, which unfortunately largely remains dependent on coarse-scale image spatial resolution, particularly for habitat modeling. For habitat-specialized species, such data may not be sufficient to successfully capture the nuances of their preferred areas. Of particular concern are those species for which topographic feature attributes are a main limiting factor for habitat use. Coarse spatial resolution data can smooth over details that may be essential for habitat characterization. Three studies focusing on sea turtle nesting beaches were completed to serve as an example of how topography can be a main deciding factor for certain species. Light Detection and Ranging (LiDAR) data were used to illustrate that fine spatial scale data can provide information not readily captured by either field work or coarser spatial scale sources. The variables extracted from the LiDAR data could successfully model nesting density for loggerhead (Caretta caretta), green (Chelonia mydas), and leatherback (Dermochelys coriacea) sea turtle species using morphological beach characteristics, highlight beach changes over time and their correlations with nesting success, and provide comparisons for nesting density models across large geographic areas. Comparisons between the LiDAR dataset and other digital elevation models (DEMs) confirmed that fine spatial scale data sources provide more similar habitat information than those with coarser spatial scales. Although these studies focused solely on sea turtles, the underlying principles are applicable for many other wildlife species whose range and behavior may be influenced by topographic features.

Boston, Massachusetts Region LIDAR First Return Elevation Data, 2002

LIDAR (LIght Detection And Ranging) first return elevation data of the Boston, Massachusetts region from MassGIS at 1-meter resolution. This LIDAR data was captured in Spring 2002. LIDAR first return data (which shows the highest ground features, e.g. tree canopy, buildings etc.) can be used to produce a digital terrain model of the Earth's surface. This dataset consists of 74 First Return DEM tiles. The tiles are 4km by 4km areas corresponding with the MassGIS orthoimage index. This data set was collected using 3Di's Digital Airborne Topographic Imaging System II (DATIS II). The area of coverage corresponds to the following MassGIS orthophoto quads covering the Boston region (MassGIS orthophoto quad ID: 229890, 229894, 229898, 229902, 233886, 233890, 233894, 233898, 233902, 233906, 233910, 237890, 237894, 237898, 237902, 237906, 237910, 241890, 241894, 241898, 241902, 245898, 245902). The geographic extent of this dataset is the same as that of the MassGIS dataset: Boston, Massachusetts Region 1:5,000 Color Ortho Imagery (1/2-meter Resolution), 2001 and was used to produce the MassGIS dataset: Boston, Massachusetts, 2-Dimensional Building Footprints with Roof Height Data (from LIDAR data), 2002 [see cross references].

Boston, Massachusetts, 2-Dimensional Building Footprints with Height Data (from LIDAR data), 2002

This dataset consists of 2D footprints of the buildings in the metropolitan Boston area, based on tiles in the orthoimage index (orthophoto quad ID: 229890, 229894, 229898, 229902, 233886, 233890, 233894, 233898, 233902, 237890, 237894, 237898, 237902, 241890, 241894, 241898, 241902, 245898, 245902). This data set was collected using 3Di's Digital Airborne Topographic Imaging System II (DATIS II). Roof height and footprint elevation attributes (derived from 1-meter resolution LIDAR (LIght Detection And Ranging) data) are included as part of each building feature. This data can be combined with other datasets to create 3D representations of buildings and the surrounding environment.

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.

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.