Este trabalho, sendo pioneiro no Brasil, teve o intuito de efetuar uma investigação das nuvens cirrus na região Metropolitana de São Paulo (23,33ºS / 46,44ºW), SP, através do sistema MSP-Lidar para o período de Junho à Julho de 2007. Durante este período, foi verificada uma ocorrência de cirrus de aproximadamente 54% sobre o total de medidas efetuadas pelo sistema Lidar. Medidas com Lidar nos forneceram uma alta resolução espacial e temporal destas nuvens, permitindo assim caracterizá-las e classificá-las de acordo com as suas propriedades macro- e microfísicas. Para obter tais parâmetros, uma metodologia própria foi desenvolvida na recuperação dos dados de Lidar e uma robusta estatística foi aplicada para determinar as diferentes classes de cirrus. A metodologia adotada se resumiu basicamente (a) na determinação de períodos estacionários (ou observações) durante a evolução temporal de detecção de cirrus, (b) determinação da base e topo através de um valor limiar para o cálculo das variáveis macrofísicas (altitudes, temperaturas, espessuras geométricas), (c) aplicação do método da transmitância para cada camada de nuvem e a determinação das variáveis microfísicas (profundidade óptica e razão de Lidar). Neste processo, a razão de Lidar é calculada iterativamente até que haja a convergência da mesma. Análises estatísticas de multivariáveis foram efetuadas para a determinação das classes de cirrus. Estas classes são baseadas na espessura geométrica, altitude média e sua respectiva temperatura, a altitude relativa (diferença entre a altura da tropopausa e topo da nuvem) e a profundidade óptica. O uso sucessivo da Análise de Componentes Principais (PCA), do Método de Cluster Hierárquico (MCH) e da Análise de Discriminantes (AD) permitiu a identificação de 4 classes. Vale ressaltar que tais métodos foram aplicados somente para os casos identificados como camadas únicas de nuvens, pois não se observou significativamente a ocorrência de nuvens com multicamadas. A origem de formação das classes de cirrus encontradas, embora apresentando propriedades macro- e microfísicas distintas, foi identificada basicamente como a mesma, isto é, provenientes da injeção de vapor dágua na atmosfera por meio de sistemas frontais e seu respectivo resfriamento para a formação dos cristais de gelo. O mesmo mecanismo de formação também é atribuído aos jatos subtropicais. Uma análise em relação ao perfil de temperatura e a comparação com a literatura mostrou que as cirrus classificadas apresentam possivelmente cristais em forma de placas e colunas hexagonais. As razões de lidar (RL) calculadas também estão de acordo com a literatura. / This pioneer work in Brazil, aimed at investigating cirrus clouds in the metropolitan region of São Paulo (23.33 ºS / 46.44 ºW), SP, observed by the MSP-Lidar system in June and July 2007. During this period, cirrus clouds were observed during approximately 54% of the time of all Lidar measurements available. The Lidar provided measurements with high spatial and temporal resolution measurements of these clouds that allowed characterizing and classifying them according to their macro-and microphysical properties. For such parameters, a unique methodology was developed for the Lidar data retrieval and a robust statistic was applied to determine the different classes of cirrus. The following steps were adopted to characterize the observations: (a) the determination of stationary periods (or observations) during the time evolution of cirrus detection, (b) determination of the base and top of clouds through a so called threshold value to derive the macrophysical variables (altitude, temperature, geometrical thickness), (c) the application of the transmittance method for each layer and the determination of cloud microphysical variables (optical depth and Lidar ratio). In this process, the Lidar ratio is calculated iteratively until a convergence of this value is achieved. Multivariate statistical analyses were performed to determine the classes of cirrus. These classes are based on geometric thickness, average altitude and the respective temperature, relative altitude (difference between tropopause height and cloud top) and optical depth. The successive use of Principal Component Analysis (PCA), Hierarchical Clustering Method (HCM) and Discriminant Analysis (DA) allowed the identification of four classes of cirrus. It is important to point out here that such methods were applied only to cases identified as single layers of clouds, due to the rare occurrence of multilayered clouds. The origin of formation for the four cirrus classes, though they have distinct macro-and microphysical properties, was found to be basically the same, i.e., from the injection of water vapor in the atmosphere provided by frontal systems, followed by the cooling process to form ice crystals. The same formation mechanism is also attributed to the subtropical jet. An analysis of the temperature profile and comparison with the literature showed that the cirrus crystals possibly have the form of hexagonal plates and columns. The Lidar Ratio (LR) was also found to be in accordance with the literature.

Indiana University-Purdue University Indianapolis (IUPUI) / An important topic in autonomous vehicle related research in recent times is road departure warning (RDW) and road keeping assistance (RKA). RDW or RKA should be able to recognize and avoid roadside objects. Standard tests are needed to evaluate the performance of RDW and RKA feature of cars from different manufacturers. To avoid damage to the cars under test and the test environment during testing, there is a need of soft, durable and reusable surrogate targets representing various real roadside objects such as curb, concrete divider and metal guardrail. These surrogate objects should have representative characteristics of real roadside objects from the point of view of various commonly used object detection sensors on the vehicles such as camera, radar and LIDAR. Transportation Active Safety Institute (TASI) at Indian University-Purdue University Indianapolis (IUPUI) is in the process of developing surrogate concrete divider, curb metal guardrail and grass that should be recognized as real roadside objects by LIDAR sensors, can be crashed without damage to the test vehicle and can be reused even after multiple crashes. The first step is to understand what the representative roadside objects should look like from the point of view of LIDAR units using laser of various wavelengths, and the next step is to design surrogate objects that successfully emulate the properties of the real roadside objects. Reflectance of an object is an important property for LIDAR detection. This thesis describes an approach for the determination of infrared reflectance property of concrete, metal guardrail and grass for different LIDAR view angles. Various samples of each of these roadside objects were evaluated. Based on these measurements, the suggested reflectance of surrogate roadside objects in the common LIDAR wavelength range of 800-1100 nm is specified. Finally, the design of surrogate roadside objects that satisfy these requirements is described, and the infrared reflectance of these surrogate objects are compared to the suggested reflectance bounds for different LIDAR view angles.

I den här rapporten undersöktes möjligheten att mäta vattenvågor med LIDAR (LIght Detection And Ranging), där en supraledande nanotråds-enkelfoton-detektor (SNSPD) användes som detektor. För att mäta vågor i labbmiljö konstruerades en våg-maskin, som kan skapa vågor av olika storlek. Olika sätt att förbättra detektion av reflekterade strålen undersöktes, dels med linser, dels med matta objekt för att simulera smuts eller alger. För att placera linserna korrekt simulerades strålgången, och olika placeringar undersöktes. En konvex och en konkav lins testades, där det framgick att den konvexa linsen kan mäta brantare vågor, och den konkava fungerar bättre på större avstånd. Linser fungerar bra när vågornas lutning är liten, men för branta vågor krävs en matt förorening på vattenytan för detektion.

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The Geoscience Laser Altimeter System (GLAS) is a waveform Lidar system carried on board the Ice, Cloud, and Elevation Satellite (ICESat). This study tested the use of GLAS data, from the L3e and L3g campaigns, to estimate total canopy height. GLAS footprint locations were sampled for ground truth. The GLAS-derived and field-derived canopy heights portrayed good correlation (R2= 0.8354). This study examined two representative fire fuel models within forests in East-Central Mississippi. GLAS waveforms were compared with field data for fire fuel models 9 and 10 of the fire fuel models described by Anderson (1982). GLAS data intensities were extracted and averaged to create predictive variables. Two variables were applied in Logistic regression to predict the probability of belonging to either fuel model (overall accuracy = 0.6875).

The repeatability of stand measurements derived from LiDAR data was tested in east-central Mississippi. Data collected from LiDAR missions and from ground plots were analyzed to estimate stand parameters. Two independent LiDAR missions were flown in approximate orthogonal directions. Field plots were generated where the missions overlapped, and tree data were taken in these plots. LiDAR data found 86-100% of mature pine trees, 64-81% of immature pine trees, and 63-72% of mature hardwood trees. Immature and mature pine tree heights measured from LiDAR were found to be significantly different (α= 0.05) than field measured heights. Individual tree volumes and plot volume for mature pines were precisely predicted in both flight directions. The results of this study showed that LiDAR repeatability in mature pines can be accurately achieved. But immature pine and hardwood plots were unable to match the repeatability of the mature pine plots.

No description available.

Digital Elevation Models (DEM) are a simple representation of the Earth’s surface. DEMs play an important role in the field of remote sensing and GIS and are used as basis for mapping and analysis for a vest majority of scientific applications. There are many ways of producing DEMs, however the direct geo-referencing technology has made Airborne Laser Scanning (ALS) a preferred technology for the acquisition of accurate surface models over broad areas. ALS uses LiDAR (Light Detection and Ranging) which uses light in a form of pulsed laser to measure distances. Before the introduction of the DEM called Ny Nationell Höjdmodell (NNH), the highest level of height data over Sweden was the GSD-altitude data (Geographical Sweden Data). The NNH was a project by Lantmäteriet, where between 2009-2019 the entire Sweden was laser scanned. The product was a new height model called Laser Data NH with positional accuracy of 0,1 m in height and relative accuracy of 0,15 m. This project focuses on testing few methods for consistency assessment between the overlapping strips using linear features. Linear features are extracted for each overlapping area, based on intersection between planar patches extracted from gable rooftops. The first method of this study computes the distance between the overlapping areas without linear features, using two approaches: cloud-to-cloud distance and mesh-to-cloud distance. The second method computes the transformation shifts and rotations needed for the linear features to align by registering the strips with both levelled and not levelled registration. In the third method, distances and angles are measured between the lines, to further analyze how well the strips fit together. The distances are measured as distance between a mid-point of one line in the first LiDAR strip and the line on the second LiDAR strip, for all linear features. The distances were measures both as 3D distances and separately as horizontal and vertical distances. As a final step a hypothesis testing was performed to determine whether the distances and angles between the lines are significant or whether any systematic error is present in the point cloud. Based on the results obtained from the first method, significant distance between the point clouds was obtained. The results from the mesh-to-cloud distance yielded better result with higher uncertainty. According to the second method significant distances between the linear features were obtained based on the registration. The mean absolute error of the registrations showed an error at a dm level, with a minimal rotation in the vertical plane for the coalignment for the levelled registration. The third method showed a mean distance between the linear features of 20 cm. Moreover, this method showed a significant inconsistence between the linear features in the vertical plane based on the high standard uncertainty. / Digitala höjdmodeller (DEM) är en enkel representation av jordens yta. DEM spelar en viktig roll inom fjärranalys och GIS och används som grund för kartläggning och analys för en majoritet av vetenskapliga tillämpningar. Det finns många sätt att producera DEM, men den direkta georefereringstekniken har gjort Airborne Laser Scanning (ALS) till en föredragen teknik för förvärv av exakta ytmodeller över breda områden. ALS använder LiDAR (Light Detection and Ranging) som använder ljus i form av pulserande laser för att mäta avstånd. Före introduktionen av Ny Nationell Höjdmodell (NNH) var den högsta nivån av höjddata över Sverige GSD-höjddata (Geographical Sweden Data). NNH var ett projekt av Lantmäteriet, där mellan 2009-2019 laserscannades hela Sverige. Produkten var en ny höjdmodell som heter Laserdata NH med positionsnoggrannhet på 0,1 m i höjd och relativ noggrannhet på 0,15 m. Detta projekt fokuserar på att testa få metoder för konsekvensbedömning mellan de överlappande remsorna med hjälp av linjära funktioner. Linjära funktioner extraheras för varje överlappande område, baserat på skärningspunkten mellan plana fläckar extraherade från gaveltak. Den första metoden för denna studie beräknar avståndet mellan de överlappande områdena utan linjära funktioner, med två metoder: moln-till-moln-avstånd och nät-till-moln-avstånd. Den andra metoden beräknar de transformationsförskjutningar och rotationer som behövs för att de linjära särdragen ska kola genom att registrera remsorna med både nivellerad och inte nivellerad registrering. I den tredje metoden mäts avstånd och vinklar mellan linjerna, för att ytterligare analysera hur bra remsorna passar ihop. Avstånden mäts som avstånd mellan en mittpunkt på en linje i den första LiDAR-remsan och linjen på den andra LiDAR-remsan, för alla linjära funktioner. Avstånden var mått både som 3D -avstånd och separat som horisontella och vertikala avstånd. Som ett sista steg utfördes en hypotesprovning för att avgöra om avstånden och vinklarna mellan linjerna är signifikanta eller om det finns något systematiskt fel i punktmolnet. Baserat på resultaten från den första metoden erhölls ett betydande avstånd mellan punktmolnen. Resultaten från mask-till-moln-avståndet gav bättre resultat med högre osäkerhet. Enligt den andra metoden erhölls betydande avstånd mellan de linjära särdragen baserat på registreringen. Det genomsnittliga absoluta felet för registreringarna visade ett fel på en dm -nivå, med en minimal rotation i det vertikala planet för samlinjering för den jämnade registreringen. Den tredje metoden visade ett medelavstånd mellan de linjära särdragen på 20 cm. Dessutom visade denna metod en signifikant inkonsekvens mellan de linjära särdragen i det vertikala planet baserat på hög standardosäkerhet.

Digital twins are virtual renditions of the actual world that include real-world assets, connections, activities, and processes. Recent developments in technologies play a key role in advancing the digital twin concept in urban planning, designing, and monitoring. Moreover, the latest developments in remote sensing technology have resulted in accurate city-scale light detection and ranging (LiDAR) data, which can be used to represent urban objects (buildings, vegetation, roads, and utilities), enabling the creation of digital twin of urban landscapes. This study aims to build a digital twin of the University of North Texas (UNT) using LiDAR and GIS data. In this research, LiDAR point clouds are used to create 3D building and vegetation modeling along with other GIS data (bicycle racks and parking areas) in creating a digital twin model. 3D Basemap solutions of ArcGIS Pro and ArcGIS Online Scene Viewer, respectively, are used to create an initial 3D urban model and build the ultimate digital twin of UNT. The emergency management floorplans of UNT buildings are incorporated into the digital twin to increase emergency management efficiency. Moreover, solar power potential for individual buildings at UNT has been estimated using the Digital Surface Model (DSM) and integrated into the digital twin model to identify the buildings with the highest solar energy capacity. This study indicates that implementing a digital twin in a university enhances campus efficiency, safety, and sustainability, serving as a central system for a smart campus and contributing to intelligent urban growth.