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Nestes últimos anos, tivemos um aumento no interesse na monitoração dos efeitos da atividade humana sobre a atmosfera e o clima no planeta. O uso de técnicas de sensoriamento remoto têm sido utilizados em diversos estudos, inclusive em estudos relacionados à mudanças globais. Um sistema LIDAR de espalhamento, primeiro deste tipo no Brasil, tem sido usado para fornecer o perfil vertical do coeficiente de retroespalhamento de aerossóis em 532 nm para altitudes de 4 a 6 km acima do nível do mar. Neste estudo, foram utilizados dados coletados no ano de 2005. Estes dados foram correlacionados com dados do fotômetro solar CIMEL e também com dados meteorológicos. Os principais resultados indicaram existir um padrão no comportamento destes dados meteorológicos e a distribuição vertical do coeficiente de extinção obtido através do LIDAR. Em períodos desfavoráveis de dispersão atmosférica, ou seja, elevação da temperatura do ar associado a queda de umidade relativa, aumento da pressão atmosférica e baixa taxa de ventilação, foi possível determinar com boa precisão a altura da Camada Limite Planetária, tanto através do perfil vertical do coeficiente de extinção quanto através da técnica da temperatura potencial. A técnica LIDAR mostrou ser um importante aliado na determinação da estrutura termodinâmica da atmosfera, auxiliando a caracterizar a evolução da CLP ao longo do dia, devido a sua boa resolução espacial e temporal. / In these last years, we had an increase in the interest in the monitoring of the effect of the human activity being on the atmosphere and the climate in the planet. The remote sensing techniques has been used in many studies, also related the global changes. A backscattering lidar system, the first of this kind in Brazil, has been used to provide the vertical profile of the aerosol backscatter coefficient at 532 nm up to an altitude of 4-6 km above sea level. In this study, data has was collected in the year of 2005. These data had been correlated with data of solar photometer CIMEL and also with meteorological data. The main results had indicated to exist a standard in the behavior of these meteorological data and the vertical distribution of the extinction coefficient gotten through LIDAR. In favorable periods of atmospheric dispersion, that is, rise of the temperature of associated air the fall of relative humidity, increase of the atmospheric pressure and low ventilation tax, was possible to determine with good precision the height of the Planetary Boundary Layer, as much through the vertical profile of the extinction coefficient how much through the technique of the vertical profile of the potential temperature. The technique LIDAR showed to be an important tool in the determination of the thermodynamic structure of the atmosphere, assisting to characterize the evolution of the CLP throughout the day, which had its good space and secular resolution.

Made available in DSpace on 2014-10-09T12:28:19Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T13:56:45Z (GMT). No. of bitstreams: 0 / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / O estudo desenvolvido nessa dissertação foi dividido em dois momentos. Na primeira parte foi apresentado a realização de uma calibração independente do sistema LIDAR Raman de vapor d\'água instalado no CLA seguindo uma metodologia desenvolvida na Howard University, baseada em uma análise cuidadosa da eficiência óptica dos componentes do sistema tendo como objetivo determinar essa eficiência e apresentar a resposta espectral do sistema. Após esse estudo, que permitiu obter um melhor entendimento da área instrumental do sistema, é apresentado, na segunda parte, uma análise preliminar das propriedades ópticas dos aerossóis na troposfera por meio da avaliação de alguns parâmetros como, por exemplo, os perfis verticais de extinção desses aerossóis, a LR e a SR, utilizando um sistema LIDAR Raman móvel desenvolvido pela Raymetrics Lidar Systems durante campanhas realizadas em alguns institutos de pesquisa no Estado de São Paulo. / Dissertacao (Mestrado) / IPEN/D / Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP / FAPESP:08/01954-0

Made available in DSpace on 2014-10-09T12:25:48Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T14:03:33Z (GMT). No. of bitstreams: 1 11315.pdf: 4284842 bytes, checksum: 94ad34802b7652ff6ef2b3584c12f794 (MD5) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / Dissertacao (Mestrado) / IPEN/D / Instituto de Pesquisas Energeticas e Nucleares, IPEN/CNEN-SP / FAPESP:01/07304-8

Made available in DSpace on 2014-10-09T12:26:24Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T14:04:20Z (GMT). No. of bitstreams: 0 / Nestes últimos anos, tivemos um aumento no interesse na monitoração dos efeitos da atividade humana sobre a atmosfera e o clima no planeta. O uso de técnicas de sensoriamento remoto têm sido utilizados em diversos estudos, inclusive em estudos relacionados à mudanças globais. Um sistema LIDAR de espalhamento, primeiro deste tipo no Brasil, tem sido usado para fornecer o perfil vertical do coeficiente de retroespalhamento de aerossóis em 532 nm para altitudes de 4 a 6 km acima do nível do mar. Neste estudo, foram utilizados dados coletados no ano de 2005. Estes dados foram correlacionados com dados do fotômetro solar CIMEL e também com dados meteorológicos. Os principais resultados indicaram existir um padrão no comportamento destes dados meteorológicos e a distribuição vertical do coeficiente de extinção obtido através do LIDAR. Em períodos desfavoráveis de dispersão atmosférica, ou seja, elevação da temperatura do ar associado a queda de umidade relativa, aumento da pressão atmosférica e baixa taxa de ventilação, foi possível determinar com boa precisão a altura da Camada Limite Planetária, tanto através do perfil vertical do coeficiente de extinção quanto através da técnica da temperatura potencial. A técnica LIDAR mostrou ser um importante aliado na determinação da estrutura termodinâmica da atmosfera, auxiliando a caracterizar a evolução da CLP ao longo do dia, devido a sua boa resolução espacial e temporal. / Dissertacao (Mestrado) / IPEN/D / Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP

M.Sc. / Light Detection and Ranging (LiDAR) provide decidedly accurate datasets with high data densities, in a very short time-span. However, the high volumes of data associated with LiDAR often require some form of data reduction to increase the data handling efficiency of these datasets, of which the latter could affect the feasibility of Digital Elevation Models (DEMs). Critically, when DEM processing times are reduced, the resultant DEM should still represent the terrain adequately. This study investigated three different data reduction techniques, (1) random point reduction, (2) grid resolution reduction, and (3) combined data reduction, in order to assess their effects on the accuracy, as well as the data handling efficiency of derived DEMs. A series of point densities of 1 %, 10 %, 25 %, 50 % and 75 % were interpolated along a range of horizontal grid resolutions (1-, 2-, 3-, 4-, 5-, 10- and 30- m). Results show that, irrespective of terrain complexity, data points can be randomly reduced up to 25 % of the data points in the original dataset, with minimal effects on the remaining dataset. However, when these datasets are interpolated, data points can only be reduced to 50 % of the original data points, before showing large deviations from the original DEM. A reduction of the grid resolution of DEMs showed that the grid resolution could be lowered to 4 metres before showing significant deviations. When combining point density reduction with grid resolution reduction, results indicate that DEMs can be derived from 75 % of the data points, at a grid resolution of 3 metres, without sacrificing more than 15 percent of the accuracy of the original DEM. Ultimately, data reduction should result in accurate DEMs that reduce the processing time. When considering the effect on the accuracy, as well as the processing times of the data reduction techniques, results indicate that resolution reduction is the most effective data reduction technique. When reducing the grid resolution to 4 metres, data handling efficiencies improved by 94 %, while only sacrificing 10 % of the data accuracy. Furthermore, this study investigated data reduction on a variety of terrain complexities and found that the reduction thresholds established by this study were applicable to both complex and non-complex terrain.

LiDAR (Light Detection and Ranging) elevation data were collected in the Panther Creek Watershed, Yamhill County, Oregon in September and December, 2007, March, 2009 and March, 2010. LiDAR derived images from the March, 2009 dataset were used to map pre-historic, historic, and active landslides. Each mapped landslide was characterized as to type of movement, head scarp height, slope, failure depth, relative age, and direction. A total of 153 landslides were mapped and 81% were field checked in the study area. The majority of the landslide deposits (127 landslides) appear to have had movement in the past 150 years. Failures occur on slopes with a mean estimated pre-failure slope of 27° ± 8°. Depth to failure surfaces for shallow-seated landslides ranged from 0.75 m to 4.3 m, with an average of 2.9 m ± 0.8 m, and depth to failure surfaces for deep-seated landslides ranged from 5 m to 75m, with an average of 18 m ± 14 m. Earth flows are the most common slope process with 110 failures, comprising nearly three quarters (71%) of all mapped deposits. Elevation changes from two of the successive LiDAR data sets (December, 2007 and March, 2009) were examined to locate active landslides that occurred between the collections of the LiDAR imagery. The LiDAR-derived DEMs were subtracted from each other resulting in a differential dataset to examine changes in ground elevation. Areas with significant elevation changes were identified as potentially active landslides. Twenty-six landslides are considered active based upon differential LiDAR and field observations. Different models are used to estimate landslide susceptibility based upon landslide failure depth. Shallow-seated landslides are defined in this study as having a failure depth equal to less than 4.6 m (15 ft). Results of the shallow-seated susceptibility map show that the high susceptibility zone covers 35% and the moderate susceptibility zone covers 49% of the study area. Due to the high number of deep-seated landslides (58 landslides), a deep-seated susceptibility map was also created. Results of the deep-seated susceptibility map show that the high susceptibility zone covers 38% of the study area and the moderate susceptibility zone covers 43%. The results of this study include a detailed landslide inventory including pre-historic, historic, and active landslides and a set of susceptibility maps identifying areas of potential future landslides.

Active remote sensing techniques, such as Light Detection and Ranging (LiDAR), have transformed the field of forestry and natural resource management in the last decade. Intensive assessments of forest resources and detailed structural assessments can now be accomplished faster and at multiple landscape scales. The ecological applications of having this valuable information at-hand are still only being developed. This work explores the use of two active remote sensing techniques, airborne and portable LiDAR for forestry applications in a rapidly changing landscape, Southeastern Coastal Pine woodlands. Understanding the strengths and weaknesses of airborne and portable LiDAR, the tools used to extract structural information, and how to apply these to managing fire regimes are key to conserving unique upland pine ecosystems. Measuring habitat structure remotely and predicting habitat suitability through modeling will allow for the management of specific species of interest, such as threatened and endangered species. Chapter one focuses on the estimation of canopy cover and height measures across a variety of conditions of secondary upland pine and hardwood forests at Tall Timbers Research Station, FL. This study is unique since it uses two independent high resolution small-footprint LiDAR datasets (years 2002 and 2008) and extensive field plot and transect sampling for validation. Chapter One explores different tools available for metric derivation and tree extraction from discrete return airborne LiDAR data, highlighting strengths and weaknesses of each. Field and LiDAR datasets yielded better correlations for stand level comparisons, especially in canopy cover and mean height data extracted. Individual tree crown extraction from airborne LiDAR data significantly under-reported the total number of trees reported in the field datasets using either Fusion/LVD and LiDAR Analyst (Overwatch). Chapter two evaluates stand structure at the site of one of the longest running fire ecology studies in the US, located at Tall Timbers Research Station (TTRS) in the southeastern U.S. Small footprint high resolution discrete return LiDAR was used to provide an understanding of the impact of multiple disturbance regimes on forest structure, especially on the 3-dimensional spatial arrangement of multiple structural elements and structural diversity indices. LiDAR data provided sensitive detection of structural metrics, diversity, and fine-scale vertical changes in the understory and mid-canopy structure. Canopy cover and diversity indices were shown to be statistically higher in fire suppressed and less frequently burned plots than in 1- and 2-year fire interval treated plots, which is in general agreement with the increase from 2- to 3-year fire return interval being considered an "ecological threshold" for these systems (Masters et al. 2005). The results from this study highlight the value of the use of LiDAR in evaluating disturbance impacts on the three-dimensional structure of pine forest systems, particularly over large landscapes. Chapter three uses an affordable portable LiDAR system, first presented by Parker et al. (2004) and further modified for extra portability, to provide an understanding of structural differences between old-growth and secondary-growth forests in the Red Hills area of southwestern Georgia and North Florida. It also provides insight into the strengths and weaknesses in structural determination of ground-based portable systems in contrast to airborne LiDAR systems. Structural plot metrics obtained from airborne and portable LiDAR systems presented some similarities (i.e. canopy cover), but distinct differences appeared when measuring canopy heights (maximum and mean heights) using these different methods. Both the airborne and portable systems were able to provide gap detection and canopy cover estimation at the plot level. The portable system, when compared to the airborne LiDAR sensor, provides an underestimation of canopy cover in open forest systems ([less than]50% canopy cover), but is more sensitive in detection of cover in hardwood woodland plots ([greater than]60% canopy cover). The strength of the portable LiDAR system lies in the detection of 3-dimensional fine structural changes (i.e. recruitment, encroachment) and with higher sensitivity in detecting lower canopy levels, often missed by airborne systems. Chapter four addresses a very promising application for fine-scale airborne LiDAR data, the creation of habitat suitability models for species of management and conservation concerns. This Chapter uses fine scale LiDAR metrics, such as canopy cover at various height strata, canopy height information, and a measure of horizontal vegetation distribution (clumped versus dispersed) to model the preferences of 10 songbirds of interest in southeast US woodlands. The results from this study highlight the rapidly changing nature of habitat conditions and how these impact songbird occurrence. Furthermore, Chapter four provides insight into the use of airborne LiDAR to provide specific management guidance to enhance the suitable habitat for 10 songbird species. The collection of studies presented here provides applied tools for the use of airborne and portable LiDAR for rapid assessment and responsive management in southeastern pine woodlands. The advantages of detecting small changes in three-dimensional vegetation structure and how these can impact habitat functionality and suitability for species of interest are explored throughout the next four chapters. The research presented here provides an original and important contribution in the application of airborne and portable LiDAR datasets in forest management and ecological studies.

Many calamities in history like hurricanes, tornado and flooding are proof to the large scale impact they cause to the life and economy. Computer simulation and GIS helps in modeling a real world scenario, which assists in evacuation planning, damage assessment, assistance and reconstruction. For achieving computer simulation and modeling there is a need for accurate classification of ground objects. One of the most significant aspects of this research is that it achieves improved classification for regions within which light detection and ranging (LiDAR) has low spatial resolution. This thesis describes a method for accurate classification of bare ground, water body, roads, vegetation, and structures using LiDAR data and aerial Infrared imagery. The most basic step for any terrain modeling application is filtering which is classification of ground and non-ground points. We present an integrated systematic method that makes classification of terrain and non-terrain points effective. Our filtering method uses the geometric feature of the triangle meshes created from LiDAR samples and calculate the confidence for every point. Geometric homogenous blocks and confidence are derived from TIN model and gridded LiDAR samples. The results from two representations are used in a classifier to determine if the block belongs ground or otherwise. Another important step is detection of water body, which is based on the LiDAR sample density of the region. Objects like tress and bare ground are characterized by the geometric features present in the LiDAR and the color features in the infrared imagery. These features are fed into a SVM classifier which detects bare-ground in the given region. Similarly trees are extracted using another trained SVM classifier. Once we obtain bare-grounds and trees, roads are extracted by removing the bare grounds. Structures are identified by the properties of non-ground segments. Experiments were conducted using LiDAR samples and Infrared imagery from the city of New Orleans. We evaluated the influence of different parameters to the classification. Water bodies were extracted successfully using density measures. Experiments showed that fusion of geometric properties and confidence levels resulted into efficient classification of ground and non-ground regions. Classification of vegetation using SVM was promising and effective using the features like height variation, HSV, angle etc. It is demonstrated that our methods successfully classified the region by using LiDAR data in a complex urban area with high-rise buildings.