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<p>Optical beam steering is of significant importance for various emerging applications such as light detection and ranging (LiDAR), free space optical communication, and holographic display. However, the development of schemes for dynamic spatio-temporal beam steering has been limited in the past. A previous study achieved dynamic and continuous angular beam steering of isolated ultrashort pulses from a mode-locked laser by using a passive metasurface emulating a diffraction grating followed by a lens. In this thesis, we experimentally demonstrate dynamic spatio-temporal steering of high repetition rate pulse trains using a spatial array of frequency combs with a uniform gradient in their carrier-envelope offsets. To accomplish this, we leverage the capabilities of a virtually imaged phased array (VIPA), which is a side-entrance Fabry-Perot etalon, and employ successive spatial Fourier transforms facilitated by a 4f optical lens system. Our experimental results successfully demonstrate the periodic scanning of ultrashort pulse trains generated from an electro-optic comb at a repetition rate of ~10 GHz. The scanning occurs in discrete steps of ~115 μm and ~20 ps in the spatial and temporal domains, respectively.</p>

Flooding is a worldwide problem that every year causes substantial damage for the environment and stakeholders nearby, and this impact relates to several of the United Nations Sustainable Development Goals. Colombia is specially prone to flooding as 17% of its surface area is at risk of extreme flooding. In addition, there is something called a POT (plan de ordenamiento territorial) for every municipality in Colombia, which states how the territory should be managed. For this project the rivers were of particular interest, and the POT states that no temporary or permanent constructions are allowed within 30 meters on either side of a river. The purpose of this report was to investigate and analyze the possibilities of using UAV (unmanned aerial vehicle) -based photogrammetry and UAV-based LiDAR (light detection and ranging) technology to gather sufficient data for a model that could simulate different flooding scenarios in the examined area. Data from the UAV-based photogrammetry resulted in a complete visual overview of the examined area. The data gathered from the UAV-based light detection and ranging resulted in an accurate point cloud that could be processed into a DTM (digital terrain model) where three different flooding scenarios were simulated. The simulations and the visual model showed that majority of people in theexamined area were disobeying the POT and the 30 meter rule, therefore being in risk of flooding and impacting the natural diversity of the body of water. The simulation also showed that stakeholders close to the body of water were affected for each of the three different water level scenarios. In some cases, it was only vegetation and crops that got affected by the flooding scenario, while in other cases entire structures and buildings were damaged due to the increase of water level. To complement the flooding scenarios, interviews were conducted with people that have good knowledge of the area and of ecology, resulting in a stakeholder analysis. This provided an additional depth to the analysis and showed the complexity in the management of flooding in the area.

Indiana University-Purdue University Indianapolis (IUPUI) / Despite growing recognition as a significant natural resource, methods for accurately estimating urban tree canopy cover extent and change over time are not well-established. This study evaluates new methods and data sources for mapping urban tree canopy cover, assessing the potential for increased accuracy by integrating high-resolution satellite imagery and 3D imagery derived from LIDAR and stereoscopic sensors. The results of urban tree canopy classifications derived from imagery, 3D data, and vegetation index data are compared across multiple urban land use types in the City of Indianapolis, Indiana. Results indicate that incorporation of 3D data and vegetation index data with high resolution satellite imagery does not significantly improve overall classification accuracy. Overall classification accuracies range from 88.34% to 89.66%, with resulting overall Kappa statistics ranging from 75.08% to 78.03%, respectively. Statistically significant differences in accuracy occurred only when high resolution satellite imagery was not included in the classification treatment and only the vegetation index data or 3D data were evaluated. Overall classification accuracy for these treatment methods were 78.33% for both treatments, with resulting overall Kappa statistics of 51.36% and 52.59%.

This diploma thesis solves the analysis of the influence of calibration and the method of strips alignment on the geometric accuracy of a point cloud acquired by UAV lidar scanning. The aim was to find out the influence of individual used methods, respectively various combinations. The effect of the design of the cross-flights has also been added. The evaluation was performed using standard deviations of the distances corresponding to the areas scanned in different point bands. Furthermore, verification was performed by comparing checkpoints. The results show that there is no dependence between the individual combinations. The only case was a larger displacement of the point cloud at the edge of the scanned strip in the case of cross-flights.

Modern forestry research and management emphasize infusing management practices with an understanding of natural disturbance regimes -- often called ecological forestry. Forestry practices emulating aspects of natural disturbance regimes are considered an effective tool to balance silvicultural and ecological objectives. Size, shape, and spatial distribution of canopy gaps formed by Hurricane Michael were studied across multiple site factors in a longleaf pine (Pinus palustris Mill.) woodland in southwest Georgia. No significant differences were observed in gap size or shape among landscape factors, but spatial distribution of gaps differed among landscape factors. The results observed highlight the ecological importance of the event and provide some insight into interactions at the landscape level. The implementation of a large, rapid, single disturbance event as a model for ecological silviculture may be more practically applied than disturbances such as lightning or insects which occur over longer timeframes.

Mapping is one of the main components of autonomous robots, and consist in the construction of a model of their environment based on the information gathered by different sensors over time. Those maps will have different attributes depending on the type of sensor used for the reconstruction. In this thesis we focus on RGBD cameras and LiDARs. The acquired data with cameras is dense, but the range is short and the construction of large scale and consistent maps is more challenging. LiDARs are the exact opposite, they give sparse data but can measure long ranges accurately and therefore support large scale mapping better. The thesis presents a method that uses both types of sensors with the purpose of combine their strengths and reduce their weaknesses. The evaluation of the system is done in an indoor environment, and with an autonomous robot. The result of the thesis shows a map that is robust in large environments and has dense information of the surroundings. / Kartläggning är en av huvudkomponenterna för autonoma robotar, och består av att bygga en modell av miljön utifrån informationen som samlats in av olika sensorer över tid. Dessa kartor kommer att ha olika attribut beroende på vilken typ av sensor som används för rekonstruktionen. I denna avhandling är fokus på RGBD-kameror och LiDARs. Datan från kameror är kompakt men kan bara mäta korta sträckor och det är utmanande att konstruera storskaliga och konsistenta kartor. LiDARs är exakt motsatta, de ger gles data men kan mäta långa avstånd noggrant och stödjer därför storskalig kartering bättre. Avhandlingen presenterar en metod som använder båda typerna av sensorer i syfte att kombinera deras styrkor och minska svagheterna. Utvärderingen av systemet sker i en inomhusmiljö och med en autonom robot. Resultatet av avhandlingen visar en karta som är robust i stora miljöer och har tät information om omgivningen.

Invasion by non-native species has had significant ecological and economic impacts on a global scale. In the state of Florida, Old World climbing fern (Lygodium microphyllum) is an invasive plant listed by FLEPPC as a category one invader with significant ecological impacts that threaten native plant diversity. This species relies on existing vegetative structures for support to climb into the forest canopy and forms dense mats that cover tree crowns. This subsequently affects the resources available to other species present. Quantifying the structural changes due to the presence of this species has proved logistically difficult, especially on a large spatial scale. Airborne LiDAR (Light Detection And Ranging) technology is a form of remote sensing that measures the elevation of surfaces over a site. In this study I utilized LiDAR to calculate various forest structure metrics at Jonathan Dickinson State Park (JDSP) in Hobe Sound, Florida across various management frequencies and densities of Old World climbing fern. These data were used to quantify the degree to which this invasive species alters forest structure across these two gradients. I also recorded species composition in the field to relate how Old World climbing fern impacts native plant diversity. Structural measurements including average canopy height, height of median energy (HOME), rugosity, canopy openness, and vertical structural diversity (LHDI) were calculated for a total of three hundred 0.25ha sites stratified by invasion density and management frequency. Using a combination of univariate and multivariate statistical analyses I found that the presence of Old World Climbing fern altered the physical structure of the forest communities it invades. Higher percent cover of Old World climbing fern decreased structural diversity while increased management effort was found to mitigate those impacts. The management for Old World Climbing fern was also found to impact both species richness and diversity at JDSP. I also demonstrated that there were several species that were not found and others that were more common in the presence of Old World climbing fern and that there was a relationship between management and what species were present. The results show that both Old World climbing fern and the management practices used to control it have had significant ecological impacts on the natural communities in South Florida.

Railway Catenary systems play a crucial role in the safe and reliable transportation of goods and people throughout the world. Monitoring the catenary infrastructure is crucial for safety purposes and therefore requires inspections. However, the current inspection methods are not sufficient for detecting all possible failure modes. The use of lidar has been proposed to augment the current inspection methods. This research proposes two methods for the classification of various overhead catenary components, resulting from lidar data, both solely relying on the coordinates of the captured datapoints. The methods resulted from a literature analysis and the parameters were obtained trough experimentation with a small dataset. The methods were validated using a larger dataset of 22.5 km between Boden and Gällivare and achieved promising outcomes. The first method resulted in an F1 score of 93,37% was obtained with 87,39% accuracy, whereas the second method, using a simple morphological region filtered obtain an F1 score of 95,48% and an accuracy of 91,27%. The novel contributions of the processing of lidar data in railway infrastructure is the use of a simple morphological region filter and the use of surface variation, a geometric feature for the extraction of masts and bridges. Further research is advised into the computational efficiency and further classification of components in the overhead catenary system.