The measurement of topography is a key aspect of geomorphology research, and the prevalence of high resolution topographic data predominantly from Light Detection And Ranging (LiDAR) in the past decade has facilitated a revolution in the quantitative study of planetary surface processes. From this increased quality of data, many techniques have been developed to quantify processes occurring at diverse spatial and temporal scales; from the flow of material down a hill-slope to the uplift and subsequent erosion of mountain ranges. Such insights have identified signatures of processes imprinted on landscapes. These include physical processes such as wildfires and landslides, biological processes such as animal burrowing and tree throw, in addition to tectonic uplift and large scale sediment transport. These signatures are observed in both the morphology of hill-slopes and their connection to the channel network, thereby allowing measures of topography to provide quantitative measures of the rates of processes shaping the Earth’s surface. This thesis is concerned with the development and application of reproducible topographic analysis techniques, to yield new insights into hill-slope sediment transport and to provide accurate metrics for quantifying hill-slope properties, including hill-slope length (LH) and relief (R). The measurement of hill-slope length can be performed through the inversion of drainage density, or the analysis of slope-area plots. However, in Chapter 3 I present a method which quantifies the length of hill-slopes through the generation of hill-slope flow paths. The flow path method is shown to be the most reliable of these methods, and is able to provide measurements of the properties of individual hill-slopes, rather than the basin or landscape averaged techniques commonly employed. The topographic predictions of the LH-R relationship of the nonlinear sediment flux law, stating that the rate of sediment transport is nonlinearly dependent on hill-slope gradient, are also tested and contrasted with the predictions of a linear sediment flux law. This provides the first purely topography based test of a sediment flux law. Through the fitting of a prediction of the nonlinear flux derived model to these measurements of hill-slope length and relief, the critical gradient of each landscape, a key parameter in the nonlinear sediment flux law, is also constrained. A nondimensional framework for erosion rate and relief, which allows the comparison of hill-slopes with differing properties in order to identify landscape transience is presented in Chapter 4. This analysis technique builds upon the work performed in Chapter 3, utilizing similar measurements of hill-slope properties, including hill-slope length and relief. The software produced alongside this chapter is shown to reproduce the results of previous studies which have employed this technique. The method is employed on a new landscape in Coweeta, North Carolina where subtle evidence of topographic decay is presented, consistent with models of Miocene topographic rejuvenation in this location. A detailed sensitivity analysis of the technique is performed, highlighting the need for careful parameterization of any analysis, to ensure meaningful results. This method is also employed to estimate an average critical gradient for each landscape, presenting more evidence building upon the evidence presented in Chapter 3 that a broad range of critical gradients exist for any given landscape. The work presented in Chapter 5 attempts to constrain the limits of the geomorphic analyses presented in the previous chapters, when they are applied to low resolution topographic data. A series of topographic datasets are generated at resolutions ranging from 1 to 30 meters upon which topographic analyses are performed. I test two common channel extraction algorithms and find that a simple geometric method, which identifies tangential curvature thresholds in the landscape, provides a more accurate representation of the channel network in low resolution topographic data than a process based method which identifies the topographic signature of channel initiation. The measurement of curvature is also evaluated, and alongside the estimation of diffusivity, is shown to be sensitive to data resolution, however landscape properties also exhibit a strong control on these measurements, where the larger scale curvature signal of Gabilan Mesa, California is more robust than the sharp ridgelines of Santa Cruz Island, California. Finally, the techniques developed in Chapter 3 to measure hill-slope length and relief are tested and are shown to be robust at grid sizes up to 30 meters, with the caveat that an accurate channel network can be constrained.

Orientador: Mauricio Galo / Resumo: Um Levantamento Hidrográfico (LH) tem como principal meta a obtenção de dados para a edição e atualização de documentos náuticos, estes, voltados à segurança das atividades de navegação. Objetivando padronizar parâmetros de incerteza das cartas náuticas, a Organização Hidrográfica Internacional (OHI) define níveis mínimos de confiança para diferentes ordens. A sugestão dessas especificações foi internalizada pela Marinha do Brasil, responsável pela produção das cartas náuticas brasileiras, na NORMAM-25. Um desses parâmetros é a Incerteza Vertical Total máxima permitida, um indicador de qualidade da medição da profundidade. A informação de profundidade influencia no calado máximo permitido a uma embarcação para transitar em uma região com segurança, o que pode impactar inclusive nas limitações de transações comerciais em terminais portuários, uma vez que as profundidades estimadas com acurácia potencializam os parâmetros de operação dos portos. Por se tratar de um ambiente dinâmico, seja por ação da própria natureza ou devido a atividades antrópicas, a atualização de uma carta náutica deve ser uma preocupação constante. Como complemento à tradicional técnica de levantamento por meio de um ecobatímetro acoplado a embarcações, há a opção de se realizar um LH com o emprego da tecnologia LiDAR (Light Detection And Ranging) a partir de aeronaves, por meio de um aerolevantamento batimétrico por LiDAR (ALB – Airborne LASER Bathymetry), que operam com pulsos LASER na região verde do e... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: A Hydrographic Survey (HS) has as main goal to obtain data for editing and updating nautical documents, these, focused on the safety of navigation. In order to establish a standard of uncertainty parameters for nautical charts, the International Hydrographic Organization (IHO) defines minimum levels of confidence for different orders. The suggestion of these specifications was acknowledged by the Brazilian Navy, institution responsible to produce Brazilian nautical charts, as described in NORMAM-25. One such parameter is the maximum allowed Total Vertical Uncertainty, a quality indicator of the depth measurement. Depth information influences the maximum operational draft for a vessel to safely travel in a region, causing impact on port operations and limiting the commercial transactions. Accurately estimated depths enhance the operational parameters of the ports. Due to the aim of representing a dynamic environment, whether as consequence of the action of nature itself or because of anthropic activities, updating a nautical chart must be a constant concern. As a complement to the traditional survey technique conducted with a boat-coupled echosounder, there is the option of performing a HS using LiDAR (Light Detection And Ranging) technology from aircraft, through LiDAR aerial bathymetry (ALB - Airborne LASER Bathymetry), which operate with LASER pulses in the green region of the electromagnetic spectrum. Considering these points, this work analyzed the differences between the... (Complete abstract click electronic access below) / Mestre

Tropical peatlands have some of the highest carbon densities of any ecosystem and are under enormous development pressure. This dissertation aimed to provide better estimates of the scales and trends of ecological impacts from tropical peatland deforestation and degradation across more than 7,000 hectares of both intact and disturbed peatlands in northwestern Borneo. We combined direct field sampling and airborne Light Detection And Ranging (LiDAR) data to empirically quantify forest structures and aboveground live biomass across a largely intact tropical peat dome. The observed biomass density of 217.7 ± 28.3 Mg C hectare-1 was very high, exceeding many other tropical rainforests. The canopy trees were ~65m in height, comprising 81% of the aboveground biomass. Stem density was observed to increase across the 4m elevational gradient from the dome margin to interior with decreasing stem height, crown area and crown roughness. We also developed and implemented a multi-temporal, Landsat resolution change detection algorithm for identify disturbance events and assessing forest trends in aseasonal tropical peatlands. The final map product achieved more than 92% user’s and producer’s accuracy, revealing that after more than 25 years of management and disturbances, only 40% of the area was intact forest. Using a chronosequence approach, with a space for time substitution, we then examined the temporal dynamics of peatlands and their recovery from disturbance. We observed widespread arrested succession in previously logged peatlands consistent with hydrological limits on regeneration and degraded peat quality following canopy removal. We showed that clear-cutting, selective logging and drainage could lead to different modes of regeneration and found that statistics of the Enhanced Vegetation Index and LiDAR height metrics could serve as indicators of harvesting intensity, impacts, and regeneration stage. Long-term, continuous monitoring of the hydrology and ecology of peatland can provide key insights regarding best management practices, restoration, and conservation priorities for this unique and rapidly disappearing ecosystem.

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 2008

This study aims to present and evaluate a new method for measuring the distribution of snow within built-up environments by differencing elevations collected by an Airborne Laser Scanner (ALS) before, and during peak snow accumulation. Few efforts have been made to study the distribution of snow within built-up environments due to the false assumption that high-intensity rainfall is the main contributor to peak yearly runoff rates. Traditional techniques for measuring snow are often difficult to replicate in built-up environments due to incompatibility of methods and barriers such as buildings, roads and private property. Light Detection and Ranging (LiDAR) technology, specifically ALSs, have previously been used to characterize the distribution of snow under forest canopy, and in remote mountain environments. This study investigates and assesses the utility of high resolution, non-intrusive ALS data for estimating the depth and distribution of snow within the town of Uxbridge, Ontario. ALS flights for this study were completed before the onset of snow accumulation, as well as near peak snow accumulation for the winters of 2010 and 2011. Pre and post snow accumulation ALS measured elevations were differenced to estimate the depth of the snowpack across the entire study area at a resolution of 0.5 m. Ground measurements of snow depth were also completed within 24 hours of each of the winter flights. The LiDAR-estimated and ground-measured snow depths were compared using Spearman's rank correlation coefficient as well as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Results from this thesis show that: 1) Snow depths estimated by differencing elevations from two ALS flights show a MAE of 3 cm and an RMSE of 10 cm when compared to ground-measured snow depths. (2) There is a strong, statistically significant relationship (ρ = 0:82, p < 0:001) between LiDAR-estimated and ground-measured snow depths. (3) An average bias of -3 cm was found for the entire dataset showing an underestimation in the LiDAR-estimated snow depths most likely caused by the effects of low lying vegetation on the fall ALS measurements. The results presented in this study demonstrate that ALSs are capable of providing high spatial resolution snow depth estimates within built-up environments. Furthermore, snow depth measurements made using an ALS can be used to increase the current body of knowledge on the distribution and re-distribution of snow within built-up environments. Snow distributions measured by an ALS could also be used for future development and verification of urban hydrological models.

Pimple mounds are circular to elliptical domes with basal diameters ranging from 3 to more than 30 m, and heights of 30 cm to more than 2 m above intermound levels. For almost two centuries, the origin of these features has been speculated upon by scientists without general consensus as to one of over 30 different mechanisms suggested for their origin. These soil microfeatures can be observed throughout portions of East Texas as well as Louisiana, Arkansas, Oklahoma, and Missouri. Pimple mounds have been extensively mapped throughout East Texas as complexes covering over 1.0 million ha in 47 soil survey areas. About 600,000 ha are on Pleistocene-age geological formations. This study focused on 5,500 ha in Leon County, Texas, mapped as Rader-Derly complex and Derly-Rader complex. Rader (Aquic Paleustalfs) is on mounds and Derly (Typic Glossaqualfs) in the low intermounds. These soils are mapped primarily on terraces of the Trinity River system within the survey area. Using elevation levels published for the various fluviatile terrace deposits of the Trinity River, six groups (five terrace level groups and an upland group) were identified for analysis of mounds within the study area. Processes and factors of soil formation during the life of these features were considered using two methods ? remotely sensed elevation data and sampling data collected in the field. Size, shape, and relief of mounds were analyzed using airborne-based, remotely sensed LiDAR (Light Detection and Ranging) elevation data. Particle size distributions and pedon descriptions of mounds formed on materials of various ages were compared across the study area with special emphasis given to spatial trends. Analyses indicate a fluvial origin with pimple mound orientation corresponding to surrounding ridge and swale features of the paleoriver. Pimple mounds within the study area formed in the presence of sandy to loamy alluvial sediments and require the presence of accretionary ridge microtopography over point bar deposits. This alluvial parent material and topography were further developed by fluctuations in climate and vegetation over time. The erosional influence of bioturbation by animals and the intense rainfall and flood events which frequent the study area provided an environment in which these soil microfeatures have developed and over time exhibit increased levels of pedogenesis.

There is an increasing need for three-dimensional description for various applications such as the development of catchment areas, forest fire control and restoration. Three-dimensional information plays an indispensable role; therefore acquisition of the digital elevation models (DEMs) is the first step in these applications. LiDAR is a recent development in remote sensing with great potential for providing high resolution and accurate three-dimensional point clouds for describing terrain surface. The acquired LiDAR data represents the surface where the laser pulse is reflected from the height of the terrain and object above ground. These objects should be removed to derive the DEMs. Many LiDAR data-filtering studies are based on surface, block, and slope algorithms. These methods have been developed to filter out most features above the terrain; however, in certain situations they have proved unsatisfactory. The different algorithm based on different point of view to describe the terrain surface. The appropriate adoption of the advantages from these algorithms will develop a more complete way to derive DEMs. Knowledge-based system is developed to solve some specific problems according to the given appropriate domain knowledge. Huang (2007) proposed a Knowledge-based classification system in urban feature classification using LiDAR data and high resolution aerial imagery with 93% classification accuracy. This research proposed a knowledge-based LiDAR filtering (KBLF) as a follow-up study of Huang¡¦s study. KBLF integrates various knowledge rules derived from experts in the area of ground feature extraction using LiDAR data to increase the capability of describing terrain and ground feature classification. The filtering capability of KBLF is enhanced as expected to get better quality of referenced ground points to recover terrain height and DEMs using Inverse Distance Weighting (IDW) and Nearest Neighbor (NN) methods.

Light Detection and Ranging (Lidar) is a remote sensing technique that provides high resolution range measurements between the laser scanner and Earth’s topography. These range measurements are mapped as 3D point cloud with high accuracy (< 0.1 meters). Depending on the geometry of the illuminated surfaces on earth one or more backscattered echoes are recorded for every pulse emitted by the laser scanner. Lidar has the advantage of being able to create elevation surfaces in 3D, while also having information about the intensity of the returned pulse at each point, thus it can be treated as a spatial and as a spectral data system. The 3D elevation attributes of Lidar data are used in this study to identify possible water surface points quickly and efficiently. The approach incorporates the use of Laplacian curvature computed via wavelets where the wavelets are the first and second order derivatives of a Gaussian kernel. In computer science, a kd-tree is a space-partitioning data structure used for organizing points in a k dimensional space. The 3D point cloud is segmented by using a kd-tree and following this segmentation the neighborhood of each point is identified and Laplacian curvature is computed at each point record. A combination of positive curvature values and elevation measures is used to determine the threshold for identifying possible water surface points in the point cloud. The efficiency and accurate localization of the extracted water surface points are demonstrated by using the Lidar data for Williamson County in Texas. Six different test sites are identified and the results are compared against high resolution imagery. The resulting point features mapped accurately on streams and other water surfaces in the test sites. The combination of curvature and elevation filtering allowed the procedure to omit roads and bridges in the test sites and only identify points that belonged to streams, small ponds and floodplains. This procedure shows the capability of Lidar data for water surface mapping thus providing valuable datasets for a number of applications in geomorphology, hydrology and hydraulics. / text

The research problem that this thesis seeks to examine is a method of predicting conventional fire hazards using data drawn from specific regions, namely the Sooke and Goldstream watershed regions in coastal British Columbia. This thesis investigates whether LiDAR data can be used to describe conventional forest stand fire hazard classes. Three objectives guided this thesis: to discuss the variables associated with fire hazard, specifically the distribution and makeup of fuel; to examine the relationship between derived LiDAR biometrics and forest attributes related to hazard assessment factors defined by the Capitol Regional District (CRD); and to assess the viability of the LiDAR biometric decision tree in the CRD based on current frameworks for use. The research method uses quantitative datasets to assess the optimal generalization of these types of fire hazard data through discriminant analysis. Findings illustrate significant LiDAR-derived data limitations, and reflect the literature in that flawed field application of data modelling techniques has led to a disconnect between the ways in which fire hazard models have been intended to be used by scholars and the ways in which they are used by those tasked with prevention of forest fires. It can be concluded that a significant tradeoff exists between computational requirements for wildfire simulation models and the algorithms commonly used by field teams to apply these models with remote sensing data, and that CRD forest management practices would need to change to incorporate a decision tree model in order to decrease risk. / Graduate / 0799 / 0478 / christos@koulas.ca

This study aims to present and evaluate a new method for measuring the distribution of snow within built-up environments by differencing elevations collected by an Airborne Laser Scanner (ALS) before, and during peak snow accumulation. Few efforts have been made to study the distribution of snow within built-up environments due to the false assumption that high-intensity rainfall is the main contributor to peak yearly runoff rates. Traditional techniques for measuring snow are often difficult to replicate in built-up environments due to incompatibility of methods and barriers such as buildings, roads and private property. Light Detection and Ranging (LiDAR) technology, specifically ALSs, have previously been used to characterize the distribution of snow under forest canopy, and in remote mountain environments. This study investigates and assesses the utility of high resolution, non-intrusive ALS data for estimating the depth and distribution of snow within the town of Uxbridge, Ontario. ALS flights for this study were completed before the onset of snow accumulation, as well as near peak snow accumulation for the winters of 2010 and 2011. Pre and post snow accumulation ALS measured elevations were differenced to estimate the depth of the snowpack across the entire study area at a resolution of 0.5 m. Ground measurements of snow depth were also completed within 24 hours of each of the winter flights. The LiDAR-estimated and ground-measured snow depths were compared using Spearman's rank correlation coefficient as well as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Results from this thesis show that: 1) Snow depths estimated by differencing elevations from two ALS flights show a MAE of 3 cm and an RMSE of 10 cm when compared to ground-measured snow depths. (2) There is a strong, statistically significant relationship (ρ = 0:82, p < 0:001) between LiDAR-estimated and ground-measured snow depths. (3) An average bias of -3 cm was found for the entire dataset showing an underestimation in the LiDAR-estimated snow depths most likely caused by the effects of low lying vegetation on the fall ALS measurements. The results presented in this study demonstrate that ALSs are capable of providing high spatial resolution snow depth estimates within built-up environments. Furthermore, snow depth measurements made using an ALS can be used to increase the current body of knowledge on the distribution and re-distribution of snow within built-up environments. Snow distributions measured by an ALS could also be used for future development and verification of urban hydrological models.