ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NV / Third generation LIDAR full-waveform (FW) based systems collect 1D FW signals of the echoes generated by laser pulses of wide bandwidth reflected at the intercepted objects to construct depth profiles along each pulse path. By emitting a series of pulses towards a scene using a predefined scanning patter, a 3D image containing spatial-depth information can be constructed. Unfortunately, acquisition of a high number of wide bandwidth pulses is necessary to achieve high depth and spatial resolutions of the scene. This implies the collection of massive amounts of data which generate problems for the storage, processing and transmission of the FW signal set. In this research, we explore the recovery of individual continuous-time FW signals at sub-Nyquist rates. The key step to achieve this is to exploit the sparsity in FW signals. Doing this allows one to sub-sample and recover FW signals at rates much lower than that implied by Shannon's theorem. Here, we describe the theoretical framework supporting recovery and present the reader with examples using real LIDAR data.

Third generation full-waveform (FW) LIDAR systems image an entire scene by emitting laser pulses in particular directions and measuring the echoes. Each of these echoes provides range measurements about the objects intercepted by the laser pulse along a specified direction. By scanning through a specified region using a series of emitted pulses and observing their echoes, connected 1D profiles of 3D scenes can be readily obtained. This extra information has proven helpful in providing additional insight into the scene structure which can be used to construct effective characterizations and classifications. Unfortunately, massive amounts of data are typically collected which impose storage, processing and transmission limitations. To address these problems, a number of compression approaches have been developed in the literature. These, however, generally require the initial acquisition of large amounts of data only to later discard most of it by exploiting redundancies, thus sampling inefficiently. Based on this, our main goal is to apply efficient and effective LIDAR sampling schemes that achieve acceptable reconstruction quality of the 3D scenes. To achieve this goal, we propose on using compressive sampling by emitting pulses only into random locations within the scene and collecting only the corresponding returned FW signals. Under this framework, the number of emissions would typically be much smaller than what traditional LIDAR systems require. Application of this requires, however, that scenes contain many degrees of freedom. Fortunately, such a requirement is satisfied in most natural and man-made scenes. Here, we propose to use a measure of rank as the measure of degrees of freedom. To recover the connected 1D profiles of the 3D scene, matrix completion is applied to the tensor slices. In this paper, we test our approach by showing that recovery of compressively sampled 1D profiles of actual 3D scenes is possible using only a subset of measurements.

LiDAR (Light Detection and Ranging) is an active remote sensing system which is used to map the surface of the Earth and which can be processed to show the ground surface under the canopy cover. The aim of this study is to examine if LiDAR can be used as a method for mapping karst on the island of Gotland, what kind of karst morphologies may be identified and their geographical distribution. LiDAR was visualized in the computer platform ArcGIS and in ArcMap version 10.3 (Esri). More than 2000 karst objects were mapped using LiDAR. Of these, eight different locations including 34 potential karst objects were chosen for field control. Six different classes of karst have been identified including three classes of dolines. The results show that LiDAR can be used for mapping karst, especially the bigger karst morphologies which are easier to identify. The results from the field control show that 45 % of the small dolines, 43 % of the medium sized dolines and 33 % of the large dolines which has been mapped in LiDAR were actual dolines. For larger scale karst morphologies the success rate was almost 100 %. The method did not lend itself well to identification of sinkholes, most likely related to the very small size of these on Gotland.

The relative importance of infrequent, episodic geomorphic events (e.g. floods, landslides, debris flows, earthquakes, tsunamis, etc.) in the evolution of the landscape has been a long-discussed question in the geomorphology community. These events are large in magnitude, but low in frequency, posing the complex question of how effective these events are at shaping the landscape. Unfortunately, the frequencies of these events are so low that it is extremely difficult to observe these events over human time scales. Also, the dangerous nature of these events makes them extremely difficult to observe and measure. However, the last few decades have brought new technology and techniques that provide a way to measure and calculate the magnitudes of these events more accurately and completely. In the present study, we use Next-Generation-Radar (NEXRAD) precipitation products, LiDAR tools, and multiple denudation-rate techniques to approach the magnitude and frequency of episodic events in different ways. Using NEXRAD precipitation products in conjunction with flow-routing algorithms, we were able to improve upon the traditional flood-envelope curves used to estimate the largest possible flood for a given basin area within a region. Improvements included adding frequency and uncertainty information to curves for the Upper and Lower Colorado River Basin, which in turn makes these curves more informative for flood hazard and policy applications. This study allowed us to improve upon a known flood-analysis method for identifying the distribution of the maximum floods with basin area. Both airborne and terrestrial LiDAR methods were used to measure the magnitude and time scale of the post-wildfire erosional response in two watersheds after the Las Conchas fire of 2011 in the Valles Caldera, NM. We found that sediment yield (measured by differencing LiDAR-derived DEMs) decreased exponentially with time in one watershed, while sediment yield in the other watershed decreased in a more complex way with time. Both watersheds had a recovery time (i.e. time interval over which sediment yields recovered to pre-wildfire levels) of one year. LiDAR was also used to understand the complex response of, and the processes on, the piedmonts adjacent to the watersheds. Overall, LiDAR proved to be extremely useful in measuring the magnitude and time scale of post-wildfire geomorphic response and observing the piedmont dynamics associated with elevated sediment yield. To understand the effects of wildfire on the long-term evolution of the landscape, techniques ranging from the relatively simple, traditional techniques (i.e. suspended-sediment-load sampling and paleosurface and modern surface differencing) to more complex and new techniques (i.e. ¹⁰Be and LiDAR) were used to measure the volumes and rates of denudation over multiple time scales in the Valles Caldera, NM. Long-term denudation rates were higher than short-term, non-wildfire-affected denudation rates, but lower than short-term, wildfire-affected denudation rates. Wildfire-affected denudation rates occurring at previously predicted frequencies (occurring<3% of the time interval) were found to account for the majority of long-term denudation, attesting to the importance of these episodic and extreme events in the evolution of the landscape.

A novel design for atmospheric laser radar (lidar) is presented, implementing a digital micromirror device (DMD) for use in (A) aligning transmitter and receiver boresight angles and in (B) field-of-view (FOV) control of such "DMD lidar" instruments. A novel technique is presented to extract the transmitter-receiver overlap-compensation function from ratioing data from different FOVs in the same pointing direction. DMD lidar design considerations and trades are surveyed. Principles of modeling DMD lidar performance are introduced and implemented in a performance-predictive system simulation with data-validated results. Operational capabilities of DMD lidar are demonstrated through a hardware prototype with field measurement examples. Additional capabilities offered by integrating DMD within lidar and other optical systems are presented, including single-pixel Radon-imaging techniques.

The Province of British Columbia is developing a suite of attributes to assess and monitor forest sustainability. Each attribute is in turn evaluated using a variety of indicators. Recently, digital remote sensing technologies have emerged as both alternative and supplement to traditional monitoring techniques, with light detection and ranging (lidar) in particular showing great promise for estimating a variety of indicators. The goal of this thesis was to review and assess the ability of lidar to estimate selected indicators of forest sustainability. Specifically, digital elevation model (DEM) interpolation (from which indicators are extracted both directly and indirectly) and wildlife tree class distributions were examined. Digital elevation models are a key derivative of lidar data, and their generation is a critical step in the data processing stream. A validation exercise was undertaken to determine which combination of interpolation routine and spatial resolution was the most accurate. Ground returns were randomly subsetted into prediction and validation datasets. Linear, quintic, natural neighbour, spline with tension, regularized spline, inverse distance weighting, and ANUDEM interpolation routines were used to generate surfaces at spatial resolutions of 0.5, 1.0, and 1.5 m. The 0.5 m natural neighbour surface was found to be the most accurate (RMSE=0.17 m). Classification and regression tree analysis indicated that slope and ground return density were the best predictors of interpolation error. The amount and variability of living and dead wood in a forest stand is an important indicator of forest biodiversity. In the second study, the capacity of lidar to estimate the distribution of living and dead trees within forests is investigated. Twenty-two field plots were established in which each stem (DBH>10cm) was assigned to a wildlife tree (WT) class. For each plot, a suite of lidar-derived predictor variables were extracted. Ordinal logistic regression was then employed to predict the cumulative proportions of stems within the WT classes. Results indicated that the coefficient of variation of the lidar height data was the best predictor variable (r = 0.85, p <0.000, RMSE = 4.9%). The derived relationships allowed for the prediction of the proportion of stems within WT classes across the landscape.

Anthropogenic infrastructure such as roads, ditches and culverts have strong impacts on hydrological processes, particularly surface drainage patterns. Despite this, these structures are often not present in the digital elevation models (DEMs) used to provide surface drainage data to hydrological models, owing to the coarse spatial resolution of many available DEMs. Modelling drainage patterns in human-modified landscapes requires very accurate, high-resolution DEM data to capture these features. Light Detection And Ranging (LiDAR) is a remote sensing technique that is used for producing DEMs with fine resolutions that can represent anthropogenic landscapes features such as human modifications on the landscape such as roadside ditches. In these data, roads act as a barrier to flow and are treated as dams, where on the ground culverts and bridges exist. While possible to locate and manually enforce flow across these roads, there is currently no automated technique to identify these locations and perform flow enforcement. This research improves the modelling of surface drainage pathways in rural anthropogenic altered landscapes by utilizing a novel algorithm that identifies ditches and culverts in LiDAR DEMs and enforces flow through these features by way of breaching. This breaching algorithm was tested on LiDAR datasets for two rural test sites in Southern Ontario. These analyses showed that the technique is an effective tool for efficiently incorporating ditches and culverts into the hydrological analysis of a landscape that has both a gradient associated with it, as well as a lack of densely forested areas. The algorithm produced more accurate representations of both overland flow when compared to outputs that excluded these anthropogenic features all together.

Natural hazards related to ground movement that directly affect the safety of motorists and highway infrastructure include, but are not limited to, rockfalls, rockslides, debris flows, and landslides. This thesis specifically deals with the evaluation of rockfall hazards through the evaluation of LiDAR data. Light Detection And Ranging (LiDAR) is an imaging technology that can be used to delineate and evaluate geomechanically-controlled hazards. LiDAR has been adopted to conduct hazard evaluations pertaining to rockfall, rock-avalanches, debris flows, and landslides. Characteristics of LiDAR surveying, such as rapid data acquisition rates, mobile data collection, and high data densities, pose problems to traditional CAD or GIS-based mapping methods. New analyses methods, including tools specifically oriented to geomechanical analyses, are needed. The research completed in this thesis supports development of new methods, including improved survey techniques, innovative software workflows, and processing algorithms to aid in the detection and evaluation of geomechanically controlled rockfall hazards. The scientific research conducted between the years of 2006-2010, as presented in this thesis, are divided into five chapters, each of which has been published by or is under review by an international journal. The five research foci are: i) geomechanical feature extraction and analysis using LiDAR data in active mining environments; ii) engineered monitoring of rockfall hazards along transportation corridors: using mobile terrestrial LiDAR; iii) optimization of LiDAR scanning and processing for automated structural evaluation of discontinuities in rockmasses; iv) location orientation bias when using static LiDAR data for geomechanical analysis; and v) evaluating roadside rockmasses for rockfall hazards from LiDAR data: optimizing data collection and processing protocols. ii The research conducted pertaining to this thesis has direct and significant implications with respect to numerous engineering projects that are affected by geomechanical stability issues. The ability to efficiently and accurately map discontinuities, detect changes, and standardize roadside geomechanical stability analyses from remote locations will fundamentally change the state-of-practice of geotechnical investigation workflows and repeatable monitoring. This, in turn, will lead to earlier detection and definition of potential zones of instability, will allow for progressive monitoring and risk analysis, and will indicate the need for pro-active slope improvement and stabilization. / Thesis (Ph.D, Geological Sciences & Geological Engineering) -- Queen's University, 2010-03-26 11:25:15.741

Contractors and tunnelling engineers consistently seek to identify techniques and equipment to improve the efficiency and lower the cost of tunnelling projects. Based on the recent successes of rock slope characterization with laser scanning techniques, the author proposes 3D laser scanning (LiDAR) as a new tool for geotechnical assessment in drill and blast tunnels. It has been demonstrated that practical deployment of a phase-based LiDAR system at the face of an active tunnel heading is possible with a simple tripod setup. With data collection requiring only 5 minutes at the tunnel face, it was shown that this technique could be integrated into geotechnical evaluation without interruption of the excavation cycle. Following the successful scanning at two active tunnelling projects and two completed unlined tunnels, the research explored the applications of the data. With detailed geometric data of the heading as it advanced, the author identified applications of interest to the contractor/on-site engineer as well as the geotechnical engineer or geologist responsible for rockmass characterization. Operational applications included the extraction of information about tunnel geometry and installed support, while geomechanical information provided important elements of rockmass characterization. Building on the success of retrieving joint network information, the research investigated the potential for LiDAR-derived structural databases to be the basis for highly-representative 3D discrete element models. These representative models were found to be useful for back-analysis or as predictive tools for future tunnel design. The primary implications of the thesis are that a) LiDAR data collection at the face of a drill and blast tunnel operation is practical and potentially has great value, b) data extraction is possible for a wide range of applications, and c) that discontinuum stability analysis becomes a much more powerful tool with the integration of LiDAR data. The cumulative result of the work presented is a proposed workflow for integrating LiDAR into tunneling operations. / Thesis (Master, Geological Sciences & Geological Engineering) -- Queen's University, 2010-09-22 19:38:49.401

Leaf Area Index (LAI) is an important input variable for forest ecosystem modeling as it is a factor in predicting productivity and biomass, two key aspects of forest health. Current in situ methods of determining LAI are sometimes destructive and generally very time consuming. Other LAI derivation methods, mainly satellite-based in nature, do not provide sufficient spatial resolution or the precision required by forest managers. This thesis focused on estimating LAI from: i) height and density metrics derived from Light Detection and Ranging (LiDAR); ii) spectral vegetation indices (SVIs), in particular the Normalized Difference Vegetation Index (NDVI); and iii) a combination of these two remote sensing technologies. In situ measurements of LAI were calculated from digital hemispherical photographs (DHPs) and remotely sensed variables were derived from low density LiDAR and high resolution WorldView-2 data. Multiple Linear Regression (MLR) models were created using these variables, allowing forest-wide prediction surfaces to be created. Results from these analyses demonstrated: i) moderate explanatory power (i.e., R2 = 0.54) for LiDAR models incorporating metrics that have proven to be related to canopy structure; ii) no relationship when using SVIs; and iii) no significant improvement of LiDAR models when combining them with SVI variables. The results suggest that LiDAR models in boreal forest environments provide satisfactory estimations of LAI, even with low ranges of LAI for model calibration. On the other hand, it was anticipated that traditional SVI relationships to LAI would be present with WorldView-2 data, a result that is not easily explained. Models derived from low point density LiDAR in a mixedwood boreal environment seem to offer a reliable method of estimating LAI at a high spatial resolution for decision makers in the forestry community. / Thesis (Master, Geography) -- Queen's University, 2012-09-24 16:18:09.96