With the ever increasing population and economic activities in coastal areas, coastal hazards have become a major concern for coastal management. The fundamental requirement of coastal planning and management is the scientific knowledge about coastal forms and processes. This research aims at developing algorithms for automatically extracting coastal morphological information from LiDAR data. The primary methods developed by this research include automated algorithms for beach profile feature extraction and change analysis, and an object-based approach for spatial pattern analysis of coastal morphologic and volumetric change. Automated algorithms are developed for cross-shore profile feature extraction and change analysis. Important features of the beach profile such as dune crest, dune toe, and beach berm crest are extracted automatically by using a scale-space approach and by incorporating contextual information. The attributes of important feature points and segments are derived to characterize the morphologic properties of each beach profile. Beach profiles from different time periods can be compared for morphologic and volumetric change analysis. An object-oriented approach for volumetric change analysis is developed to identify and delineate individual elevation change patches as discrete objects. A set of two-dimensional and three-dimensional attributes are derived to characterize the objects, which includes planimetric attributes, shape attributes, surface attributes, volumetric attributes, and summary attributes. Both algorithms are implemented as ArcGIS extension modules to perform the feature extraction and attribute derivation for coastal morphological change analysis. To demonstrate the utility and effectiveness of algorithms, the cross-shore profile change analysis method and software tool are applied to a case study area located at southern Monterey Bay, California, and the coastal morphology change analysis method and software tool are applied to a case study area located on Assateague Island, Maryland. The automated algorithms facilitate the efficient beach profile feature analysis over large geographical area and support the analysis of the spatial variations of beach profile changes along the shoreline. The explicit object representation of elevation change patches makes it easy to localize erosion hot spots, to classify the elevation changes caused by various mechanisms, and to analyze spatial pattern of morphologic and volumetric changes.

The overall goal of this study was to develop a framework for using airborne lidar to derive inputs for the SPB infestation growth model TAMBEETLE. The specific objectives were (1) to estimate individual tree characteristics of XY location, individual bole height (IBH), diameter at breast height (DBH), length of crown (CrHT), and age for use in TAMBEETLE; (2) to estimate individual tree age using lidar-estimated height and site index provided by the United States Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) Soil Survey Geographic Database (SSURGO); and (3) to compare TAMBEETLE simulation results using field measurements and lidarderived measurements as inputs. Diameter at breast height, individual bole height, and crown length were estimated using lidar with an error for mean measurements at plot level of 0.16cm, 0.19m, and 1.07m, respectively. These errors were within root mean square error (RMSE) for other studies at the study site. Age was estimated using the site index provided by SSURGO and the site index curves created for the study area with an RMSE of 4.8 years for mean plot age. Underestimation of tree height by lidar and error in the site index curve explained 91% of the error in mean plot age. TAMBEETLE was used to compare spot growth between a lidar-derived forest map and a forest map generated by TAMBEETLE, based on sample plot characteristics. The lidar-derived forest performed comparably to the TAMBEETLE generated forest. Using lidar to map forests can provide the large spatial extents of the TAMBEETLE generated forest while maintaining the spatially explicit forest characteristics, which were previously only available through field measurements.

The majority of biofuels are produced from corn and grain. The drawback to these sources of biofuels is the vast amount of cultivated land needed to produce substantial amounts of biofuel, potentially increasing the price of food and livestock products. Mesquite trees, a type of woody plant, are a proven source of bioenergy feedstock found on semi-arid lands. The overall objectives of this study were to develop algorithms for determining woody plant biomass on rangelands in Texas at plot-level using terrestrial lidar and at the local scale by integrating reference biomass and multispectral imagery. Terrestrial lidar offers a more efficient method for estimating biomass than traditional field measurements. Variables from the terrestrial lidar point cloud were compared to ground measurements of biomass to find a best fitting regression model. Two processing methods were investigated for analyzing the lidar point cloud data, namely: 1) percentile height statistics and 2) a height bin approach. Regression models were developed for variables obtained through each processing technique for estimating woody plant, above-ground biomass. Regression models were able to explain 81 percent and 77 percent of the variance associated with the aboveground biomass using percentile height statistics and height bins, respectively. The aboveground biomass map was generated by using the cokriging interpolation method with NDVI and ground biomass data. According to cross-validation, ordinary cokriging estimated biomass accurately (R^2 = 0.99). The results of this study revealed that terrestrial lidar can be used to accurately and efficiently estimate the aboveground biomass of mesquite trees in a semi-arid environment at plot level. Moreover, spatial interpolation techniques proved useful in scaling up biomass estimates to local scale.

Due to the increasing availability of high-resolution remotely sensed imagery and detailed terrain surface elevation models, urban planners and municipal managers can now model and visualize the urban space in three dimensions. The traditional approach to the representation of urban space is 2D planimetric maps with building footprints, facilities and road networks. Recently, a number of methods have been developed to represent true 3D urban models. Those include panoramic imaging, Virtual Reality Modeling Language (VRML), and Computer-aided Design (CAD). These methods focus on aesthetic representation, but they do not have sufficient spatial query and analytical capabilities. This research evaluates the conventional approaches to 3D urban models, and identifies their advantages and limitations; GIS functionalities have been combined with 3D urban visualization techniques to develop a GIS-based urban modeling method; The algorithms and techniques have been explored to derive urban objects and their attributes from airborne LiDAR and high-resolution imagery for constructing and visualizing 3D urban models; and 3D urban models for the Texas A&M University (TAMU) campus and downtown Houston have been implemented using the algorithms and techniques developed in this research. By adding close-range camera images and highresolution aerial photographs as the texture of urban objects, effect of photorealism visualization has been achieved for walk-through and fly-through animations. The Texas A&M University campus model and the downtown Houston model have been implemented to offer proof-of-concept, namely, to demonstrate the advantages of the GIS-based approach. These two prototype applications show that the GIS-based 3D urban modeling method, by coupling ArcGIS and MultiGen-Paradigm Site Builder 3D software, can realize the desired functionalities in georeferencing, geographical measurements, spatial query, spatial analysis, and numerical modeling in 3D visual environment.

Global modeling of gross primary production (GPP) is a critical component of climate change research. On local scales, GPP can be assessed from measuring CO₂ exchange above the plant canopy using tower-based eddy covariance (EC) systems. The limited footprint inherent to this method however, restricts observations to relatively few discrete areas making continuous predictions of global CO₂ fluxes difficult. Recently, the advent of high resolution optical remote sensing devices has offered new possibilities to address some of the scaling issues related to GPP using remote sensing. One key component for inferring GPP spectrally is the efficiency (ε) with which plants can use absorbed photosynthetically active radiation to produce biomass. While recent years have seen progress in measuring ε using the photochemical reflectance index (PRI), little is known about the temporal and spatial requirements for up-scaling these findings continuously throughout the landscape. Satellite observations of canopy reflectance are subject to view and illumination effects induced by the bi-directional reflectance distribution function(BRDF) which can confound the desired PRI signal. Further uncertainties include dependencies of PRI on canopy structure, understorey, species composition and leaf pigment concentration. The objective of this research was to investigate the effects of these factors on PRI to facilitate the modeling of GPP in a continuous fashion. Canopy spectra were sampled over a one-year period using an automated tower-based, multi-angular spectroradiometer platform (AMSPEC), designed to sample high spectral resolution data. The wide range of illumination and viewing geometries seen by the instrument permitted comprehensive modeling of the BRDF. Isolation of physiologically induced changes in PRI yielded a high correlation (r²=0.82, p<0.05) to EC-measured ε, thereby demonstrating the capability of PRI to model ε throughout the year. The results were extrapolated to the landscape scale using airborne laser-scanning (light detection and ranging, LiDAR) and high correlations were found between remotely-sensed and EC-measured GPP (r²>0.79, p<0.05). Permanently established tower-based canopy reflectance measurements are helpful for ongoing research aimed at up-scaling ε to landscape and global scales and facilitate a better understanding of physiological cycles of vegetation and serve as a calibration tool for broader band satellite observations.

Recent studies have demonstrated that images constructed from lidar reflectance information exhibit superior robustness to lighting changes. However, due to the scanning nature of the lidar and assumptions made in previous implementations, data acquired during continuous vehicle motion suffer from geometric motion distortion and can subsequently result in poor metric visual odometry (VO) estimates, even over short distances (e.g., 5-10 m). The first part of this thesis revisits the measurement timing assumption made in previous systems, and proposes a frame-to-frame VO estimation framework based on a pose-interpolation scheme that explicitly accounts for the exact acquisition time of each intrinsic, geometric feature measurement. The second part of this thesis investigates a novel method of lidar calibration that can be applied without consideration of the internal structure of the sensor. Both methods are validated using experimental data collected from a planetary analogue environment with a real scanning laser rangefinder.

Previously disturbed and regenerating forests make up a significant proportion of the North American land area, and therefore play an important role in the exchanges of heat and trace gases between the terrestrial biosphere and the atmosphere. Assessment of local to global variability in CO2 exchanges by forests requires a combination of CO2 measurements made by eddy covariance (EC), field measurements, remote sensing data, and ecosystem models. The integration of these is problematic because of a mis-match in scale between measurement techniques. Despite the importance of regenerating forests on the global carbon balance, the processes affecting the carbon cycle within these forests is not well understood. Airborne scanning light detection and ranging (lidar) instruments provide new opportunities to examine three-dimensional forest characteristics from the level of individual trees to ecosystems and beyond. Lidar is therefore an effective link between plot measurements, eddy covariance, and low resolution remote sensing pixels. This thesis dissertation presents new science on the use of airborne lidar for evaluating remote sensing products within heterogeneous and previously clearcut ecosystems. The goals of this thesis were to first understand the processes affecting CO2 exchanges within a previously disturbed boreal jack pine chronosequence located in Saskatchewan, Canada and then to apply this understanding to evaluate low resolution remote sensing data products from the Moderate Resolution Imaging Spectroradiometer (MODIS) using airborne lidar. The first objective of this dissertation examined the factors that control light use efficiency (LUE) within the jack pine chronosequence during dry and wet years. The second objective examined the importance of vegetation structure and ground surface elevation on CO2 fluxes within a mature jack pine forest. The third objective developed and tested a simple model of lidar fractional cover and related this to the fraction of photosynthetically active radiation absorbed by the canopy (fPAR). This was then used to evaluate the MODIS fPAR product across the lower part of a watershed. Finally, the fourth objective was to model gross primary production (GPP) from airborne lidar. Lidar estimates of GPP were then compared with those from the EC system at the jack pine chronosequence and with the MODIS GPP (Collection 5) product. / Thesis (Ph.D, Geography) -- Queen's University, 2008-08-22 08:50:51.44

Three dimensional laser scanning, also known as Light Detection and Ranging (LiDAR) has quickly been expanding in its applications in the field of geological engineering due to its ability to rapidly acquire highly accurate three dimensional positional data. Recently is has been shown that LiDAR scanning can be easily integrated into an excavation sequence in an underground environment for the purpose of collecting rockmass and discontinuity information. As scans are often taken multiple times of the same environment, the next logical application of LiDAR scanning is for monitoring for change and deformation. Traditionally, deformation and change in an underground environment is measured using a series of five or more permanent control points installed around the profile of an excavation. Using LiDAR for profile analysis provides many benefits as compared to traditional monitoring techniques. Due to the high density of the point cloud data, the change in profile is able to be fully characterized, and areas of anomalous movement can easily be separated from overall closure trends. Furthermore, monitoring with LiDAR does not require the permanent installation of control points, therefore monitoring can be completed more quickly after excavation, and scanning is non-invasive therefore no damage is done during the installation of temporary control points. The main drawback of using LiDAR scanning for deformation monitoring is that the raw point accuracy is generally the same magnitude as the smallest level of deformations that need to be measured. To overcome this, statistical techniques for profile analysis must be developed. This thesis outlines the development one such method, called the Elliptical Fit Analysis (EFA) and LiDAR Profile Analysis (EFA) for tunnel and shaft convergence analysis. Testing of the EFA and LPA has proved the robustness of this technique in its ability to deal with accuracy and precision issues associated with LiDAR scanning. / Thesis (Master, Geological Sciences & Geological Engineering) -- Queen's University, 2012-05-15 13:24:28.398

Recent studies have demonstrated that images constructed from lidar reflectance information exhibit superior robustness to lighting changes. However, due to the scanning nature of the lidar and assumptions made in previous implementations, data acquired during continuous vehicle motion suffer from geometric motion distortion and can subsequently result in poor metric visual odometry (VO) estimates, even over short distances (e.g., 5-10 m). The first part of this thesis revisits the measurement timing assumption made in previous systems, and proposes a frame-to-frame VO estimation framework based on a pose-interpolation scheme that explicitly accounts for the exact acquisition time of each intrinsic, geometric feature measurement. The second part of this thesis investigates a novel method of lidar calibration that can be applied without consideration of the internal structure of the sensor. Both methods are validated using experimental data collected from a planetary analogue environment with a real scanning laser rangefinder.

Light Detection And Ranging (LiDAR) mapping has been emerging over the past few years as a mainstream tool for the dense acquisition of three dimensional point data. Besides the conventional mapping missions, LiDAR systems have proven to be very useful for a wide spectrum of applications such as forestry, structural deformation analysis, urban mapping, and reverse engineering. The wide application scope of LiDAR lead to the development of many laser scanning technologies that are mountable on multiple platforms (i.e., airborne, mobile terrestrial, and tripod mounted), this caused variations in the characteristics and quality of the generated point clouds. As a result of the increased popularity and diversity of laser scanners, one should address the heterogeneous LiDAR data post processing (i.e., registration and segmentation) problems adequately. Current LiDAR integration techniques do not take into account the varying nature of laser scans originating from various platforms. In this dissertation, the author proposes a methodology designed particularly for the registration and segmentation of heterogeneous LiDAR data. A data characterization and filtering step is proposed to populate the points’ attributes and remove non-planar LiDAR points. Then, a modified version of the Iterative Closest Point (ICP), denoted by the Iterative Closest Projected Point (ICPP) is designed for the registration of heterogeneous scans to remove any misalignments between overlapping strips. Next, a region-growing-based heterogeneous segmentation algorithm is developed to ensure the proper extraction of planar segments from the point clouds. Validation experiments show that the proposed heterogeneous registration can successfully align airborne and terrestrial datasets despite the great differences in their point density and their noise level. In addition, similar testes have been conducted to examine the heterogeneous segmentation and it is shown that one is able to identify common planar features in airborne and terrestrial data without resampling or manipulating the data in any way. The work presented in this dissertation provides a framework for the registration and segmentation of airborne and terrestrial laser scans which has a positive impact on the completeness of the scanned feature. Therefore, the derived products from these point clouds have higher accuracy as seen in the full manuscript.