Discrimination of different tree crowns based on their 3D shapes is essential for a wide range of forestry applications, and, due to its complexity, is a significant challenge. This study presents a modified 3D shape descriptor for the perception of different tree crown shapes in discrete-return LiDAR point clouds. The proposed methodology comprises of five main components, including definition of a local coordinate system, learning salient points, generation of simulated LiDAR point clouds with geometrical shapes, shape signature generation (from simulated LiDAR points as reference shape signature and actual LiDAR point clouds as evaluated shape signature), and finally, similarity assessment of shape signatures in order to extract the shape of a real tree. The first component represents a proposed strategy to define a local coordinate system relating to each tree to normalize 3D point clouds. In the second component, a learning approach is used to categorize all 3D point clouds into two ranks to identify interesting or salient points on each tree. The third component discusses generation of simulated LiDAR point clouds for two geometrical shapes, including a hemisphere and a half-ellipsoid. Then, the operator extracts 3D LiDAR point clouds of actual trees, either deciduous or evergreen. In the fourth component, a longitude-latitude transformation is applied to simulated and actual LiDAR point clouds to generate 3D shape signatures of tree crowns. A critical step is transformation of LiDAR points from their exact positions to their longitude and latitude positions using the longitude-latitude transformation, which is different from the geographic longitude and latitude coordinates, and labeled by their pre-assigned ranks. Then, natural neighbor interpolation converts the point maps to raster datasets. The generated shape signatures from simulated and actual LiDAR points are called reference and evaluated shape signatures, respectively. Lastly, the fifth component determines the similarity between evaluated and reference shape signatures to extract the shape of each examined tree. The entire process is automated by ArcGIS toolboxes through Python programming for further evaluation using more tree crowns in different study areas. Results from LiDAR points captured for 43 trees in the City of Surrey, British Columbia (Canada) suggest that the modified shape descriptor is a promising method for separating different shapes of tree crowns using LiDAR point cloud data. Experimental results also indicate that the modified longitude-latitude shape descriptor fulfills all desired properties of a suitable shape descriptor proposed in computer science along with leaf-off, leaf-on invariance, which makes this process autonomous from the acquisition date of LiDAR data. In summary, the modified longitude-latitude shape descriptor is a promising method for discriminating different shapes of tree crowns using LiDAR point cloud data.

O Laboratório de Aplicações Ambientais a Laser, situado no Centro de Lasers e Aplicações no IPEN (Instituto de Pesquisas Energéticas e Nucleares), efetua medidas das concentrações de aerossóis atmosféricos, enviando um feixe de laser à atmosfera e coletando a luz retroespalhada. Tal sistema fornece um grande número de parâmetros físicos que devem ser administrados de forma ágil para a obtenção de análises resultantes. Em conseqüência disso, a implementação de um banco de dados tornou-se imprescindível como instrumento de comunicação e visualização gráfica das medidas coletadas. Um critério de classificação destas valiosas informações foi adotado, estabelecendo níveis de armazenamento definidos a partir de características específicas aos tipos de dados determinados. A compilação e automação destas medidas promoverá a integração entre dados, análise e retorno otimizado de resultados das propriedades da atmosfera, propiciando futuras pesquisas e análise de dados. / The LIDAR system (Light Detection and Ranging) laser remote sensing at the Nuclear and Energy Research Institute Laboratory of Environmental Laser Applications allows on line measurements of variations in the concentrations of atmospheric aerosols by sending a laser beam to the atmosphere and collecting the backscattered light. Such a system supplies a great number of physical parameters that must be managed in an agile form to the attainment of a real time analysis. Database implementation therefore becomes an important toll of communication and graphical visualization of measurements. A criterion for classification of this valuable information was adopted, establishing defined levels of storage from specific characteristics of the determined data types. The compilation and automation of these measurements will promote optimized integration between data, analysis and retrieval of the resulting properties and of the atmosphere, improving future research and data analysis.

Lidar is able to provide height and cover information which can be used to estimate selected forest attributes precisely. However, for users to evaluate whether the additional cost and complication associated with using Lidar merits adoption requires that the protocol to use lidar be thoroughly described and that a basis for selection of design parameters such as number of field plots and lidar pulse density be described. In our first analysis, we examine these issues by looking at the effects of pulse density and sample size on estimation when wall-to-wall lidar is used with a regression estimator. The effects were explored using resampling simulations. We examine both the effects on precision, and on the validity of inference. Pulse density had almost no effect on precision for the range examined, from 3 to .0625 pulses / m��. The effect of sample size on estimator precision was roughly in accordance with the behavior indicated by the variance estimator, except that for small samples the variance estimator had positive bias (the variance estimates were too small), compromising the validity of inference. In future analyses we plan to provide further context for wall-to-wall lidar-assisted estimation. While there is a lot of literature on modeling, there is limited information on how lidar-assisted approaches compare to existing methods, and what variables can or cannot be acquired, or may be acquired with reduced confidence. We expand our investigation of estimation in our second analysis by examining lidar obtained in a sampling mode in combination with Landsat. In this case we make inference about the feasibility of a lidar-assisted estimation strategy by contrasting its variance estimate with variance estimates from a variety of other sampling designs and estimators. Of key interest was how the precision of a two-stage estimator with lidar strips compared with a plot-only estimator from a simple random sampling design. We found that because the long and narrow lidar strips incorporate much of the landscape variability, if the number of lidar strips was increased from 7 to 15 strips, the precision of estimators with lidar can exceed that of estimators applied to plot-only SRS data for a much larger number of plots. Increasing the number of lidar strips is considered to be highly viable since the costs of field plots can be quite expensive in Alaska, often exceeding the cost of a lidar strip. A Landsat-assisted approach used for either an SRS or a two-stage sample was also found to perform well relative to estimators for plot-only SRS data. This proved beneficial when we combined lidar and Landsat-assisted regression estimators for two-stage designs using a composite estimator. The composite estimator yielded much better results than either estimator used alone. We did not assess the effects of changing the number of lidar strips in combination with using a composite estimator, but this is an important analysis we plan to perform in a future study. In our final analysis we leverage the synergy between lidar and Landsat to improve the explanatory power of auxiliary Landsat using a multilevel modeling strategy. We also incorporate a more sophisticated approach to processing Landsat which reflects temporal trends in individual pixels values. Our approach used lidar as an intermediary step to better match the spatial resolution of Landsat and increase the proportion of area overlapped between measurement units for the different sources of data. We developed two separate approaches for two different resolutions of data (30 m and 90 m) using multiple modeling alternatives including OLS and k nearest neighbors (KNN), and found that both resolution and the modeling approach affected estimates of residual variability, although there was no combination of model types which was a clear winner for all responses. The modeling strategies generally fared better for the 90 m approaches, and future analyses will examine a broader range of resolutions. Fortunately the approaches used are fairly flexible and there is nothing prohibiting a 1000 m implementation. In the future we also plan to look at using a more sophisticated Landsat time-series approach. The current approach essentially dampened the noise in the temporal trend for a pixel, but did not make use of information in the trend such as slope or indications of disturbance ��� which may provide additional explanatory power. In a future study we will also incorporate a multilevel modeling into estimation or mapping strategies and evaluate the contribution of the multilevel modeling strategy relative to alternate approaches. / Graduation date: 2013 / Access restricted to the OSU Community at author's request from June 21, 2012 - Dec. 21, 2012

Submitted by Pedro Silva Filho (pfsilva@ipen.br) on 2018-01-31T16:58:28Z No. of bitstreams: 0 / Made available in DSpace on 2018-01-31T16:58:28Z (GMT). No. of bitstreams: 0 / A relação entre a poluição atmosférica e a saúde vem sendo estudada há muitos anos em grandes cidades ao redor de todo o mundo e recentemente em regiões de intensa queima de biomassa. Porto Nacional é um município do estado do Tocantins que vem sofrendo expressivo crescimento em decorrência da expansão da cultura de grãos. Está incluso no bioma cerrado, onde é costume o uso do fogo para limpeza dos campos de agricultura. O lidar é uma ferramenta que vem sendo usada para estudo do perfil óptico atmosférico mundialmente, não havendo registros de sua utilização no cerrado. Junto com o lidar, costuma-se também utilizar a análise de filtros de impactação de aerossóis para determinação da composição e concentração de material particulado, o qual está relacionado ao desenvolvimento de doenças respiratórias.Também não havia sido investigada a correlação das queimadas no Tocantins com a saúde respiratória da população. Esse estudo visa analisar o perfil óptico e químico dos aerossóis provenientes das queimadas na região e correlacionar as internações por doenças respiratórias com o número de focos de incêndio no município de Porto Nacional e no estado do Tocantins. Para isso, foram utilizados um sistema lidar móvel, dados dos sistemas MODIS e CALIPSO e filtros de impactação de aerossóis instalados em Porto Nacional, dados de queimadas do Instituto Nacional de Pesquisas Espaciais e de saúde do banco de dados do Departamento de Informática do Sistema Único de Saúde. Foram registradas com o uso do lidar camadas de aerossóis em agosto de 2015 a uma altitude de 2 a 3,5 km, com predomínio de poeira contaminada, resultado da mistura de poeira da crosta e produto de queimadas. A análise química dos aerossóis mostrou aumento de black carbon e óxidos de alumínio e sílicio em setembro/2013 e aumento de óxido de enxofre em dezembro de 2015. Através das análises de modelos lineares generalizados e correlação de Pearson, não foi encontrada relação entre o número de focos de incêndio e as internações por doenças respiratórias, apesar da significância estatística dos dados colhidos ter sido confirmada pelo stepwise. Sugere-se aprofundamento do estudo através da coleta direta de dados de saúde respiratória diários e de atendimento em pronto socorro. / Dissertação (Mestrado em Tecnologia Nuclear) / IPEN/D / Instituto de Pesquisas Energéticas e Nucleares - IPEN-CNEN/SP

O Laboratório de Aplicações Ambientais a Laser, situado no Centro de Lasers e Aplicações no IPEN (Instituto de Pesquisas Energéticas e Nucleares), efetua medidas das concentrações de aerossóis atmosféricos, enviando um feixe de laser à atmosfera e coletando a luz retroespalhada. Tal sistema fornece um grande número de parâmetros físicos que devem ser administrados de forma ágil para a obtenção de análises resultantes. Em conseqüência disso, a implementação de um banco de dados tornou-se imprescindível como instrumento de comunicação e visualização gráfica das medidas coletadas. Um critério de classificação destas valiosas informações foi adotado, estabelecendo níveis de armazenamento definidos a partir de características específicas aos tipos de dados determinados. A compilação e automação destas medidas promoverá a integração entre dados, análise e retorno otimizado de resultados das propriedades da atmosfera, propiciando futuras pesquisas e análise de dados. / The LIDAR system (Light Detection and Ranging) laser remote sensing at the Nuclear and Energy Research Institute Laboratory of Environmental Laser Applications allows on line measurements of variations in the concentrations of atmospheric aerosols by sending a laser beam to the atmosphere and collecting the backscattered light. Such a system supplies a great number of physical parameters that must be managed in an agile form to the attainment of a real time analysis. Database implementation therefore becomes an important toll of communication and graphical visualization of measurements. A criterion for classification of this valuable information was adopted, establishing defined levels of storage from specific characteristics of the determined data types. The compilation and automation of these measurements will promote optimized integration between data, analysis and retrieval of the resulting properties and of the atmosphere, improving future research and data analysis.

Population estimation in inter-censual years has many important applications. In this research, high-resolution pan-sharpened IKONOS image, LiDAR data, and parcel data are used to estimate small-area population in the eastern part of the city of Denton, Texas. Residential buildings are extracted through object-based classification techniques supported by shape indices and spectral signatures. Three population indicators -building count, building volume and building area at block level are derived using spatial joining and zonal statistics in GIS. Linear regression and geographically weighted regression (GWR) models generated using the three variables and the census data are used to estimate population at the census block level. The maximum total estimation accuracy that can be attained by the models is 94.21%. Accuracy assessments suggest that the GWR models outperformed linear regression models due to their better handling of spatial heterogeneity. Models generated from building volume and area gave better results. The models have lower accuracy in both densely populated census blocks and sparsely populated census blocks, which could be partly attributed to the lower accuracy of the LiDAR data used.

Airborne discrete-return lidar is an active remote sensing technology capable of obtaining accurate, fine-resolution three-dimensional measurements over large areas. Discrete-return lidar data produce three-dimensional object characterizations in the form of point clouds defined by precise x, y and z coordinates. The data also provide intensity values for each point that help quantify the reflectance and surface properties of intersected objects. These data features have proven to be useful for the characterization of many important forest attributes, such as standing tree biomass, height, density, and canopy cover, with new applications for the data currently accelerating. This dissertation explores three new applications for airborne discrete-return lidar data. The first application uses lidar-derived metrics to predict understory vegetation cover, which has been a difficult metric to predict using traditional explanatory variables. A new airborne lidar-derived metric, understory lidar cover density, created by filtering understory lidar points using intensity values, increased the coefficient of variation (R²) from non-lidar understory vegetation cover estimation models from 0.2-0.45 to 0.7-0.8. The method presented in this chapter provides the ability to accurately quantify understory vegetation cover (± 22%) at fine spatial resolutions over entire landscapes within the interior ponderosa pine forest type. In the second application, a new method for quantifying and locating snags using airborne discrete-return lidar is presented. The importance of snags in forest ecosystems and the inherent difficulties associated with their quantification has been well documented. A new semi-automated method using both 2D and 3D local-area lidar point filters focused on individual point spatial location and intensity information is used to identify points associated with snags and eliminate points associated with live trees. The end result is a stem map of individual snags across the landscape with height estimates for each snag. The overall detection rate for snags DBH ≥ 38 cm was 70.6% (standard error: ± 2.7%), with low commission error rates. This information can be used to: analyze the spatial distribution of snags over entire landscapes, provide a better understanding of wildlife snag use dynamics, create accurate snag density estimates, and assess achievement and usefulness of snag stocking standard requirements. In the third application, live above-ground biomass prediction models are created using three separate sets of lidar-derived metrics. Models are then compared using both model selection statistics and cross-validation. The three sets of lidar-derived metrics used in the study were: 1) a 'traditional' set created using the entire plot point cloud, 2) a 'live-tree' set created using a plot point cloud where points associated with dead trees were removed, and 3) a 'vegetation-intensity' set created using a plot point cloud containing points meeting predetermined intensity value criteria. The models using live-tree lidar-derived metrics produced the best results, reducing prediction variability by 4.3% over the traditional set in plots containing filtered dead tree points. The methods developed and presented for all three applications displayed promise in prediction or identification of unique forest attributes, improving our ability to quantify and characterize understory vegetation cover, snags, and live above ground biomass. This information can be used to provide useful information for forest management decisions and improve our understanding of forest ecosystem dynamics. Intensity information was useful for filtering point clouds and identifying lidar points associated with unique forest attributes (e.g., understory components, live and dead trees). These intensity filtering methods provide an enhanced framework for analyzing airborne lidar data in forest ecosystem applications. / Graduation date: 2013

Improved monitoring of forest biomass is needed to quantify natural and anthropogenic effects on the terrestrial carbon cycle. Landsat's temporal and spatial coverage, fine spatial grain, and long history of earth observations provide a unique opportunity for measuring biophysical properties of vegetation across large areas and long time scales. However, like other multi-spectral data, the relationship between single-date reflectance and forest biomass weakens under certain canopy conditions. Because the structure and composition of a forest stand at any point in time is linked to the stand's disturbance history, one potential means of enhancing Landsat's spectral relationships with biomass is by including information on vegetation trends prior to the date for which estimates are desired. The purpose of this research was to develop and assess a method that links field data, airborne lidar, and Landsat-derived disturbance and recovery history for mapping of forest biomass and biomass change. Our study area is located in eastern Oregon (US), an area dominated by mixed conifer and single species forests. In Chapter 2, we test and demonstrate the utility of Landsat-derived disturbance and recovery metrics to predict current forest structure (live and dead biomass, basal area, and stand height) for 51 field plots, and compare the results with estimates from airborne lidar and single-date Landsat imagery. To characterize the complex nature of long-term (insect, growth) and short-term (fire, harvest) vegetation changes found in this area, we use annual Landsat time series between 1972 and 2010. This required integrating Landsat data from MSS (1972-1992) and TM/ETM+ (1982-present) sensors. In Chapter 2, we describe a method to bridge spectral differences between Landsat sensors, and therefore extent Landsat time-series analyses back to 1972. In Chapter 3, we extend and automate our approach and develop maps of current (2009) and historic (1993-2009) live forest biomass. We use lidar data for model training and evaluate the results with forest inventory data. We further conduct a sensitivity analysis to determine the effects of forest structure, time-series length, terrain and sampling design on model predictions. Our research showed that including disturbance and recovery trends in empirical models significantly improved predictions of forest biomass, and that the approach can be applied across a larger landscape and across time for estimating biomass change. / Graduation date: 2012 / Access restricted to the OSU Community at author's request from Nov. 29, 2011 - Nov. 29, 2012

The Glass Buttes volcanic complex is a cluster of bimodal (basalt-rhyolite), Miocene to Pleistocene age lava flows and domes located in Oregon's High Lava Plains province, a broad region of Cenozoic bimodal volcanism in south-central Oregon. The High Lava Plains is deformed by northwest-striking faults of the Brothers Fault Zone, a diffuse, ~N40°W trending zone of en echelon faults cutting ~250 km obliquely across the High Lava Plains. Individual fault segments within the Brothers Fault Zone are typically <20 km long, strike ~N40°W, have apparent normal separation with 10-100 m throw. A smaller population of ~5-10 km long faults striking ~N30°E exhibits mutually crosscutting relationships with the dominant northwest striking faults. Basaltic volcanic rocks in the Glass Buttes area erupted during the late Miocene and Pleistocene. The oldest and youngest lavas are 6.49±0.03 Ma and 1.39±0.18 Ma, respectively, based on ⁴⁰Ar/³⁹Ar ages of five basaltic units. Numerous small mafic vents both within and around the margins of the main silicic dome complex are commonly localized along northwest-striking faults of the Brothers Fault Zone. These vents erupted a diverse suite of basalt to basaltic andesite lava flows that are here differentiated into 15 stratigraphic units based on hand sample texture and mineralogy as well as major and trace element geochemistry. The structural fabric of the Glass Buttes area is dominated by small displacement, discontinuous, en echelon, northwest-striking fault scarps that result from normal to slightly oblique displacements and are commonly linked by relay ramps. Northwest alignment of basaltic and rhyolitic vents, paleotopography, and cross-cutting relationships suggest these faults have been active since at least 6.49±0.03 Ma, the age of the rhyolite lavas in the eastern Glass Buttes are. Faults displace Quaternary sedimentary deposits indicating these structures continue to be active into the Quaternary. Long-term extension rates across northwest-striking faults calculated from 2-5 km long cross section restorations range from 0.004 – 0.02 mm/yr with an average of 0.12 mm/yr. A subordinate population of discontinuous northeast-striking faults form scarps and exhibit mutually cross-cutting relationships with the dominant northwest-striking population. Cross-cutting relationships indicate faulting on northeast-striking faults ceased sometime between 4.70±0.27 Ma and 1.39±0.18 Ma. Gravity data at Glass Buttes reveals prominent northwest- and northeast-trending gravity gradients that closely parallel the strikes of surface faults. These are interpreted as large, deep-seated, normal faults that express themselves in the young basalts at the surface as the discontinuous, en echelon fault segments seen throughout the study area and BFZ in general. Elevated geothermal gradients are localized along these deep-seated structures at two locations: (1) where northwest- and northeast-striking faults intersect,(2) along a very prominent northwest-striking active normal fault bounding the southwest flank of Glass Butte. High average heat flow and elevated average geothermal gradients across the High Lava Plains, and the presence of hydrothermal alteration motivated geothermal resource exploration at Glass Buttes. Temperature gradient drilling by Phillips Petroleum and others between 1977-1981 to depths of up to 600 m defined a local geothermal anomaly underlying the Glass Buttes volcanic complex with a maximum gradient of 224 °C/km. Stratigraphic constraints indicate that near-surface hydrothermal alteration associated with mercury ores ceased before 4.70±0.27 Ma, and is likely associated with the 6.49±0.03 Ma rhyolite eruptions in the eastern part of Glass Buttes. The modern thermal anomaly is not directly related to the pre-4.70±0.27 Ma hydrothermal system; rather it is likely a result of deep fluid circulation along major extensional faults in the area. / Graduation date: 2013 / Includes accompanying DVD with digital data supplement (8 GB).

ANP; FINEP; MCT; CAPES / Este trabalho objetiva otimizar a localização de um robô móvel para a inspeção dos tanques de armazenamento de GLP -- Gás Liquefeito de Petróleo -- e permitir a navegação autônoma deste robô por toda a superfície do tanque. A falta de marcos no interior dos tanques levou ao desenvolvimento de uma solução envolvendo detecção de pequenas estruturas com sensores LIDAR (Light Detection and Ranging - Detecção e Telemetria por Luz) aplicada aos cordões de solda, estes sim presentes no interior do tanque, que é apresentada na primeira parte do trabalho. Em seguida, aplicando uma técnica de fusão de dados, as diferentes fontes de odometria presentes no robô são combinadas, permitindo uma precisão mais elevada na inspeção de modo geral. Por fim, o mapeamento e navegação simultâneos do exterior da esfera é abordado, a fim de se adicionar uma camada suplementar ao mapa digital, indicando os locais onde existem falhas. Testes para validação de cada uma das técnicas foram efetuados e uma análise de desempenho é apresentada ao final do trabalho. / This work aims to optimize localization of a climbing inspection robot for spherical LPG -- Liquified Petroleum Gas -- tanks and allow autonomous navigation along the entire surface of these tanks. One solution envolving small structures detection using LIDAR sensors is applied to the weld beads that are present inside the tanks. This solution is developed on the first part of this work. Following, a data fusion technique is used to combine the diferent odometry sources on the robot, resulting in a better, higher precision on the inspection as a whole. Finnaly, simultaneous mapping and navigation on the exterior of the spherical tank was studied in order to add one extra layer to the digital map, pinpointing the places where failures and weld beams were found. Validation tests for each one of this techniques are carried out and a performance analysis is also documented herein.