Due to infrequent and imprecise maintenance inspection in power-line corridors, accidents can be caused by interferences, for instance, surrounding trees. Transmission power-line inspection conventionally relies on the participation of ground personnel and airborne camera to patrol power-lines, and is limited by intensive labour, and difficult working conditions and management. Airborne light detection and ranging (LiDAR) has proven a powerful tool to overcome these limitations to enable more efficient inspection. Active airborne LiDAR systems directly capture the 3D information of power infrastructure and surrounding objects. This study aims at building a semi-automatic 3D reconstruction workflow for power-lines extracted from airborne LiDAR data of 138 kV transmission line corridors (500 m by 340 m) in Nanaimo, BC, Canada. The proposed workflow consists of three components: detection, extraction, and fitting. The power-lines are automatically detected with regular geometric shape using a set of algorithms, including density-based filtering, Hough transform and concatenating algorithm. The complete power-lines are then extracted using a rectangular searching technique. Finally, the 3D power-lines are reconstructed through fitting by a hyperbolic cosine function and least-squares fitting. A case study is carried out to evaluate the proposed workflow for hazard tree detection in the corridor. The results obtained demonstrate that power-lines can be reconstructed in 3D, which are useful in detection of hazard trees to support power-line corridor management.

No description available.

The eastern side of the Guadalupe Mountains, located in New Mexico and west Texas, represents an erosional profile along the Capitan reef margin. A complete shelf-to-basin exposure of the Upper Permian Capitan shelf margin is found on the north wall of North McKittrick Canyon, which is nearly perpendicular to the Capitan reef margin. An excellent 2-D sequence stratigraphic framework for upper Permian backreef facies has been developed by previous workers for North McKittrick Canyon (Tinker, 1998) and Slaughter Canyon (Osleger, 1998), forming the basis for observations in this study. The goal of this study is to describe the sequence stratigraphic architecture of the Yates Formation, focusing on the Y4-Y6 high-frequency sequences (HFSs) found in the middle to outer shelf depositional systems, and to illustrate the use of airborne lidar data to quantitatively map at the cycle-scale. Seven measured sections were taken in North McKittrick Canyon. From airborne lidar, 3-D geometries of key sedimentary and structural features were mapped in Polyworks, in addition to the sequence boundaries delineating the Yates 4-6 HFSs. In general, major cycles exhibit asymmetry and shoal upward. Cycle boundaries are sometimes hard to delineate due to amalgamation, particularly in the shelf crest. High-frequency sequences are commonly asymmetric; they deepen and thicken upward toward the maximum flooding surface, and the boundaries between HFSs are usually marked by thick siltstones. Major HFS boundaries can be mapped across the entire dataset, and some component cycles can be observed for minimum distances of one kilometer in an updip-downdip direction. Also, some facies tract dimensions can be estimated directly from the lidar data. Measured sections indicate that the shelf crest facies tract shifts seaward with each successive HFS, while the outer shelf facies tract steps landward. Future work that could be done with the Y4-Y6 HFSs includes 8-10 more measured sections, collection of samples for thin sections, and tracing out of contacts between facies tracts. Extensive lidar data interpretation needs to be done so that digital outcrop models demonstrating facies distributions can be produced. This would enable the development of an outcrop analog model to mixed carbonate-siliciclastic reservoirs, which would be unprecedented in this area. / text

Highway assets, including traffic signs, traffic signals, light poles, and guardrails, are important components of transportation networks. They guide, warn and protect drivers, and regulate traffic. To manage and maintain the regular operation of the highway system, state departments of transportation (DOTs) need reliable and up-to-date information about the location and condition of highway assets. Different methodologies have been employed to collect road inventory data. Currently, ground-based technologies are widely used to help DOTs to continually update their road database, while air-based methods are not commonly used. One possible reason is that the initial investment for air-based methods is relatively high; another is the lack of a systematic and effective approach to extract road features from raw airborne light detection and ranging (LiDAR) data and aerial image data. However, for large-area inventories (e.g., a whole state highway inventory), the total cost of using aerial mapping is actually much lower than other methods considering the time and personnel needed. Moreover, unmanned aerial vehicles (UAVs) are easily accessible and inexpensive, which makes it possible to reduce costs for aerial mapping. The focus of this project is to analyze the capability and strengths of airborne data collection system in highway inventory data collection. In this research, a field experiment was conducted by the Remote Sensing Service Laboratory (RSSL), Utah State University (USU), to collect airborne data. Two kinds of methodologies were proposed for data processing, namely ArcGIS-based algorithm for airborne LiDAR data, and MATLAB-based procedure for aerial photography. The results proved the feasibility and high efficiency of airborne data collection method for updating highway inventory database.

台灣地區地層下陷問題肇始於六十年代迄今已逾三十餘載，持續下陷面積已達1,194平方公里，最大累積下陷量達到三公尺以上。而目前地層下陷地表監測所採用的傳統水準測量耗費人力、時間較多，且不易獲得連續和全面性之地層下陷資料，目前國內水利單位限於人力時間，無法針對所有監測區域每年皆施測一次。近年來由於空載光達測量技術興起，其具有短時間內獲取大區域高密度、高精度高程資料的特性，因此本研究之目的在探討如何利用空載光達測量技術快速獲取高精度之三維點雲資訊，進行大區域的地層下陷監測及其成效。 研究方法係首先將監測區內掃瞄的光達點雲資料進行網格化分群；接著，計算網格區域內所有光達點擬合平面的中心高程；然後，以人工或自動方法萃取出平坦、穩固的網格區域做為監測面；最後，進行不同時期網格監測面高程差異之統計測試分析，以求出地層下陷量。 實驗結果顯示改善點雲高程精度至5公分以內後，經由網格監測面的精度、坡度、坡向、反射強度、道路範圍等為門檻值，可萃取出80%以上正確率的穩固監測面，且其高差成果與長期監測成果的平均值差異在1.3公分至2.9公分之間，由此成果可以說明本研究成果對建立一套省時省力的監測模式，進而達到地層下陷監測自動化的目的有相當幫助。 / The issue of land subsidence in Taiwan has been concerned for over 30 years since 1970. Land subsidence area has been already over 1194 km2, the maximum amount of accumulative subsidence is more than 3 meters. The conventional leveling for the land subsidence monitoring is labor-intensive and time-consuming, so that the Water Resources Agency could not monitor all the subsidence area every year. Airborne LIDAR technology was developed in recent years, it has the characteristics of collecting 3-D point data at the high density and high elevation accuracy in short time. The purpose of this study, therefore, is to discuss how to utilize the airborne LIDAR technology to monitor the land subsidence. The proposed approach, therefore, is first to divide the collecting DSM points in the monitor area into regular grids. Secondly, all the points in the regular grids are fitted to one set planar parameters by least squares principle and the centric elevation of each grid is calculated. Third, the flatness and well-defined planar grids are selected as the monitoring surfaces with the manual or automatic method. Finally, the difference of centric elevation in each monitoring surfaces at different period is calculated and analyzed with statistical approach. This study shows that after refining the elevation accuracy of point clouds within 5 cm, our approach can extract stable monitoring surfaces by limiting planar fitting accuracy, flatness, slope, intensity, or by using road information. The extracted correct rate can be more than 80%. The discrepancy of elevation difference between this study and long-term monitoring result is between 1.3 cm and 2.9 cm. It proves the proposed approach is helpful on constructing the monitoring model in timesaving and efficient way, and our proposed approach has the potential for developing automatic land subsidence monitoring method.

3D semantic segmentation is an expanding topic within the field of computer vision, which has received more attention in recent years due to the development of more powerful GPUs and the newpossibilities offered by deep learning techniques. Simultaneously, the amount of available spatial LiDAR data over Sweden has also increased. This work combines these two advances and investigates if a 3D deep learning model for semantic segmentation can learn to detect terrain roughness in airborne LiDAR data. The annotations for terrain roughness used in this work are taken from SGUs 2D soil type map. Other airborne data sources are also used to filter the annotations and see if additional information can boost the performance of the model.  Since this is the first known attempt at terrain roughness classification from 3D data, an initial test was performed where fields were classified. This ensured that the model could process airborne LiDAR data and work for a terrain classification task. The classification of fields showed very promising results without any fine-tuning. The results for the terrain roughness classification task show that the model could find a pattern in the validation data but had difficulty generalizing it to the test data. The filtering methods tested gave an increased mIoU and indicated that better annotations might be necessary to distinguish terrain roughness from other terrain types. None of the features obtained from the other data sources improved the results and showed no discriminating abilities when examining their individual histograms. In the end, more research is needed to determine whether terrain roughness can be detected from LiDAR data or not.

Dans le contexte général de l’érosion de la biodiversité, deux espèces d’oiseaux forestiers, le Grand Tétras (Tetrao urogallus) et la Gélinotte des bois (Bonasa bonasia), présentes dans le massif Jurassien sont menacées par la perte et la fragmentation de leur habitat à l’échelle régionale. En particulier, dans le massif Jurassien l’extension progressive des tâches de régénération du hêtre induit la transformation du couvert végétal constitué de myrtilles et d’herbacées favorable en un habitat fermé défavorable. Le destin de ces deux espèces emblématiques dépend pour la première d’actions de gestions et pour la seconde d’une meilleure connaissance de la distribution et de la dynamique des populations. La coupe des zones de régénération fait partie des principales actions envisagées pour restaurer l’habitat forestier. Cependant ces actions de gestion ou de suivi des populations sont couteuses en temps et en argent. Ainsi, l’opportunité d’utiliser deux jeux de données LiDAR (Light Detection and Ranging) couvrant la majorité de l’aire de distribution des deux espèces dans le massif Jurassien a initié le projet de cartographie des habitats de chaque espèce et de la présence des tâches de régénération du hêtre en utilisant des modèles de distribution d’espèces (SDMs). L’objectif est de soutenir les gestionnaires dans leurs décisions et actions grâce à la production de prédictions spatiales adaptées. La réalisation de cet objectif dépend de la fiabilité des modèles produits, mais aussi de la bonne transmission des résultats par le chercheur aux gestionnaires qui ne sont pas familiers avec les méthodes utilisées. Dans un premier temps, le choix d’une méthode de modélisation appropriée (correction du biais d’échantillonnage, échelles, algorithmes) par rapport aux caractéristiques des jeux de données et aux objectifs a été évalué. Dans un second temps, l’utilisation de variables environnementales LiDAR orienté-objet (arbres et trouées) pour faciliter l’appropriation des résultats par les gestionnaires a été testée. Enfin, les résultats obtenus ont permis la création de modèles multi-échelles et de carte de prédictions pour chacune des espèces démontrant la capacité du LIDAR de représenter la structure de la végétation qui influence la présence des espèces d’oiseaux forestières étudiées. Des modèles de distribution de la régénération du hêtre ont pu aussi être créés à une échelle fine. / In the general context of biodiversity erosion, two forest bird species occurring in the French Jura massif, the Capercaillie (Tetrao urogallus) and the Hazel Grouse (Bonasa bonasia), are threatened by habitat loss and fragmentation at the regional scale. In particular, intensive beech regeneration patches extension in the Jura massif is leading to the transformation of the understory cover, once suitable with bilberry and herbaceous vegetation, to closed unfavorable habitat. The fate of those two emblematic species is depending for the first on future management actions and for the second on a better knowledge of the species population’s dynamics and occurrences. In particular, the cutting of the beech regeneration patches is one of the efficient management actions undertaken to restore the habitat. However, management actions and surveys are money and time consuming due to the large area that need to be covered. The opportunity to use two Light Detection and Ranging (LiDAR) datasets covering a major part of the distribution of the two species in the Jura massif initiated the phD project, with the objective to support managers in their decisions and actions by the creation of adapted distribution predicted maps using Species Distribution Models (SDMs) (Hazel Grouse, Capercaillie and beech regeneration). The realization of this objective is depending on the reliability of the models produced and on the capacity of the researcher to transfer the results to managers who are not familiar with modeling methods. In a first step, the choice of the appropriate modeling method regarding the datasets characteristics and the objectives was investigated (sampling bias correction, scales, and algorithms). In addition, the use of object-oriented LiDAR predictors (trees and gaps) pertinent from both species and managers point of view to facilitate the results transfer was tested. The results obtained were used to create appropriate multi-scale SDMs and to predict distribution maps for both target species, demonstrating the capacity of LiDAR to represent vegetation structures that influence the targeted forest bird species occurrences. Models at a fine scale were also created to map the beech regeneration distribution in the Jura massif.

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

An Airborne Mobile Aerosol Lidar (AMALi) was constructed and built at Alfred-Wegener-Institute for Polar and Marine Research (AWI) in Potsdam, Germany for the lower tropospheric aerosol and cloud research under tough arctic conditions. The system was successfully used during two AWI airborne field campaigns, ASTAR 2004 and SVALEX 2005, performed in vicinity of Spitsbergen in the Arctic. The novel evaluation schemes, the Two-Stream Inversion and the Iterative Airborne Inversion, were applied to the obtained lidar data. Thereby, calculation of the particle extinction and backscatter coefficient profiles with corresponding lidar ratio profiles characteristic for the arctic air was possible. The comparison of these lidar results with the results of other in-situ and remote instrumentation (ground based Koldewey Aerosol Raman Lidar (KARL), sunphotometer, radiosounding, satellite imagery) allowed to provided clean contra polluted (Arctic Haze) characteristics of the arctic aerosols. Moreover, the data interpretation by means of the ECMWF Operational Analyses and small-scale dispersion model EULAG allowed studying the effects of the Spitsbergens orography on the aerosol load in the Planetary Boundary Layer. With respect to the cloud studies a new methodology of alternated remote AMALi measurements with the airborne in-situ cloud optical and microphysical parameters measurements was proved feasible for the low density mixed-phase cloud studies. An example of such approach during observation of the natural cloud seeding (feeder-seeder phenomenon) with ice crystals precipitating into the lower supercooled stratocumulus deck were discussed in terms of the lidar signal intensity profiles and corresponding depolarisation ratio profiles. For parts of the cloud system characterised by almost negligible multiple scattering the calculation of the particle backscatter coefficient profiles was possible using the lidar ratio information obtained from the in-situ measurements in ice-crystal cloud and water cloud. / Das Airborne Mobile Aerosol Lidar (AMALi) wurde am Alfred-Wegener-Institut für Polar- und Meeresforschung in Potsdam für die Untersuchung arktischer Aerosole und Wolken der unteren Troposphäre entwickelt und gebaut. Das AMALi wurde erfolgreich in zwei AWI Flugzeugmesskampagnen, der ASTAR 2004 und der SvalEx 2005, die in Spitzbergen in der Arktis durchgeführt wurden, eingesetzt. Zwei neue Lidar Datenauswertungsmethoden wurden implementiert: die Two-Stream Inversion und die Iterative Airborne Inversion. Damit erwies sich die Berechnung der Profile der Teilchen Rückstreu- und Extinktionskoeffizienten mit einem entsprechenden Lidar Verhältnis, das charakteristisch für arktische Luft ist, als möglich. Der Vergleich dieser Auswertungen mit den Resultaten, die mit verschiedenen Fernerkundungs- und In-situ Instrumenten gewonnen worden waren (stationäres Koldewey Aerosol Raman Lidar KARL, Sonnenphotometer, Radiosondierung und Satellitenbilder) ermöglichten die Interpretation der Lidar-Resultate und eine Charakterisierung sowohl der reinen als auch der verschmutzten Luft. Außerdem konnten die Lidardaten mit operationellen ECMWF Daten und dem kleinskaligen Dispersionsmodel EULAG verglichen werden. Dadurch konnte der Einfluss der Spitzbergener Orographie auf die Aerosolladung der Planetaren Grenzschicht untersucht werden. Für Wolkenmessungen wurde eine neue Methode der alternativen Fernerkundung mit dem AMALi und flugzeuggetragenen In-situ Messgeräten verwendet, um optische und mikrophysikalische Eigenschaften der Wolken zu bestimmen. Diese Methode wurde erfolgreich implementiert und auf Mixed-Phase Wolken geringer optischen Dicke angewendet. Ein Beispiel hier stellt das Besamen der Wolken (sogenannte Feeder-Seeder Effekt) dar, bei dem Eiskristalle in eine niedrige unterkühlte Stratokumulus fallen. Dabei konnten Lidarsignale, Intensitätsprofile und die Volumendepolarisation gemessen werden. Zusätzlich konnten in den weniger dichten Bereichen der Wolken, in denen Vielfachstreuung vernachlässigbar ist, auch Profile des Teilchen Rückstreukoeffizienten berechnet werden, wobei Lidarverhältnisse genommen wurden, die aus In-situ Messungen für Wasser- und Eiswolken ermittelt wurden.

Les rivières en tresses constituent des plaines alluviales complexes constituées d'une mosaïque d'unités correspondant à des échelles spatio-temporelles différentes. L'objectif de cette thèse a été d'utiliser des données de LiDAR aéroporté pour améliorer la connaissance des réponses morphologiques des lits en tresses à différentes échelles spatio-temporelles. Dans un premier temps, 2 levés LiDAR séquentiels ont permis de détecter les changements morphologiques d’une tresse de 7 km survenus suite à une crue de période de retour 14 ans. Ces travaux ont été réalisés sur le site du Bès, un affluent de la Bléone. Les résultats ont mis en évidence l’importance des différentes étapes de traitement de l’information dans le calcul du bilan sédimentaire (réalignement des nuages de points séquentiels, évaluation de la bathymétrie, variabilité spatiale de l’incertitude altimétrique). L’exploitation des résultats a de plus montré un profond remaniement des chenaux tressés, du fait de l’occurrence de nombreuses avulsions. Dans un deuxième temps, les données LiDAR ont été utilisées pour caractériser la signature morphologique des lits en tresses à l’échelle plurikilométrique. L’analyse a porté sur un linéaire de plus de 25 km réparti sur 9 sites, dans les bassins versants de la Drôme, du Drac et de la Bléone. Premièrement, ces données mettent clairement en évidence l’effet du confinement de la tresse sur ses propriétés morphologiques avec entre autres un élargissement de la bande active à l'amont de ces zones. Deuxièmement, deux périodes caractéristiques ont été mises en évidence : autour de 3-4 et de 9-10 fois la largeur de la bande active. La période à 3-4 serait liée à la dynamique des macroformes. La période à 10 pourrait être liée à la dynamique de transfert à long terme des sédiments et pourrait correspondre aux successions longitudinales des mégaformes sédimentaires. Finalement, les données de LIDAR aérien ont été couplées à une étude diachronique de photographies aériennes pour reconstruire l'historique de formation des différentes unités spatiales composant la plaine d'inondation et relier cette structure avec les caractéristiques des unités de végétation. 3 rivières en tresses ont été étudiées dans les Alpes françaises avec un degré croissant d'activité : le Bouinenc, la Drôme et le Bès. Cette méthodologie a permis de reconstruire les différentes phases d'incision du lit avec deux périodes majeurs : avant 1948 et seconde partie du 20ème siècle. Il a aussi été montré l'impact des crues sur l'incision et l'élargissement de la bande active en lien avec le régime sédimentaire. Ces changements à long terme jouent un rôle significatif pour expliquer la mosaïque de la végétation de la plaine d'inondation avec une végétation bien développée et composée majoritairement d'unité matures dans le cas d'une rétraction et d'une incision sur le long terme. Les rivières plus actives présentent une diversité d'unité de végétation plus équilibrée. Enfin, la présence de lande arbustive semble être un bon indicateur des périodes d'incision. / Braided rivers form complex floodplains composed of sedimentary deposits mosaics, which differ in term of spatial and time scales, in function of hydrologic forcing and sediment supply. The goal of this thesis is to use airborne LiDAR to improve our understanding of braided channel morphological responses at different spatial and time scales.In a first time, two sequential airborne LiDAR surveys were used to reconstruct morphological changes of a 7-km-long braided river channel following a 14-year return period flood. This was done on the Bès River, a tributary of the Bléone River in southeastern France. Results highlighted the importance of different data processing steps in sediment budget computation (surface matching, bathymetry assessment, spatially distributed propagation of uncertainty). Analysis of these data also shows that the braided channel pattern was highly disturbed by the flood owing to the occurrence of several channel avulsions.In a second time, LiDAR data were used to look at longitudinal signatures of cross-sectional morphology at the scale of several kilometers. This study was done on 9 study reaches distributed on five braided rivers in Drôme, Drac and Bléone catchments. These data highlighted the effect of braided river confinement/obstruction on morphologic signature with upstream widening pattern. Secondly, two characteristic wavelengths have been identified from these signals: 3-4 and 10 times the active channel width. The first could be link to the dynamics of macroforms. The second could be associated to the dynamics of megaforms and long term sediment transfer.Finally, airborne LiDAR data were associated with archived aerial photos to reconstruct floodplain formation and relate this geomorphic organisation with vegetation patch characters. This is achieved on 3 different braided rivers in French Alps with an increasing degree of activity: the Bouinenc Torrent, the Drôme River and the Bès River. This methodology allowed us to establish the timing of channel incision with the identification of two major periods: before 1948 and second part of 20th century. Impacts of flood history on channel incision and widening pattern were also highlighted. These long term changes are playing a significant role to explain vegetation mosaics with a well-developed vegetated floodplain mainly composed of mature units following long term narrowing and incision. Rivers with higher activity show an equi-diversity of floodplain vegetation units. Finally, presence of shrubland patches seems to be good indicator of incision periods.