Literatura académica sobre el tema "Iterative Airborne Lidar Inversion"

Abstract. The Airborne Mobile Aerosol Lidar (AMALi) is an instrument developed at the Alfred Wegener Institute for Polar and Marine Research for reliable operation under the challenging weather conditions at the Earth's polar regions. Since 2003 the AMALi has been successfully deployed for measurements in ground-based installation and zenith- or nadir-pointing airborne configurations during several scientific campaigns in the Arctic. The lidar provides backscatter profiles at two wavelengths (355/532 nm or 1064/532 nm) together with the linear depolarization at 532 nm, from which aerosol and cloud properties can be derived. This paper presents the characteristics and capabilities of the AMALi system and gives examples of its usage for airborne and ground-based operations in the Arctic. As this backscatter lidar normally does not operate in aerosol-free layers special evaluation schemes are discussed, the nadir-pointing iterative inversion for the case of an unknown boundary condition and the two-stream approach for the extinction profile calculation if a second lidar system probes the same air mass. Also an intercomparison of the AMALi system with an established ground-based Koldewey Aerosol Raman Lidar (KARL) is given.

Information for individual trees (e.g., position, treetop, height, crown width, and crown edge) is beneficial for forest monitoring and management. Light Detection and Ranging (LiDAR) data have been widely used to retrieve these individual tree parameters from different algorithms, with varying successes. In this study, we used an iterative Triangulated Irregular Network (TIN) algorithm to separate ground and canopy points in airborne LiDAR data, and generated Digital Elevation Models (DEM) by Inverse Distance Weighted (IDW) interpolation, thin spline interpolation, and trend surface interpolation, as well as by using the Kriging algorithm. The height of the point cloud was assigned to a Digital Surface Model (DSM), and a Canopy Height Model (CHM) was acquired. Then, four algorithms (point-cloud-based local maximum algorithm, CHM-based local maximum algorithm, watershed algorithm, and template-matching algorithm) were comparatively used to extract the structural parameters of individual trees. The results indicated that the two local maximum algorithms can effectively detect the treetop; the watershed algorithm can accurately extract individual tree height and determine the tree crown edge; and the template-matching algorithm works well to extract accurate crown width. This study provides a reference for the selection of algorithms in individual tree parameter inversion based on airborne LiDAR data and is of great significance for LiDAR-based forest monitoring and management.

Abstract A physical inversion scheme has been developed dealing with cloudy as well as cloud-free radiance observed with ultraspectral infrared sounders to simultaneously retrieve surface, atmospheric thermodynamic, and cloud microphysical parameters. A fast radiative transfer model, which applies to the clouded atmosphere, is used for atmospheric profile and cloud parameter retrieval. A one-dimensional (1D) variational multivariable inversion solution is used to improve an iterative background state defined by an eigenvector-regression retrieval. The solution is iterated in order to account for nonlinearity in the 1D variational solution. It is shown that relatively accurate temperature and moisture retrievals can be achieved below optically thin clouds. For optically thick clouds, accurate temperature and moisture profiles down to cloud-top level are obtained. For both optically thin and thick cloud situations, the cloud-top height can be retrieved with relatively high accuracy (i.e., error <1 km). National Polar-orbiting Operational Environmental Satellite System (NPOESS) Airborne Sounder Testbed Interferometer (NAST-I) retrievals from the The Observing-System Research and Predictability Experiment (THORPEX) Atlantic Regional Campaign are compared with coincident observations obtained from dropsondes and the nadir-pointing cloud physics lidar (CPL). This work was motivated by the need to obtain solutions for atmospheric soundings from infrared radiances observed for every individual field of view, regardless of cloud cover, from future ultraspectral geostationary satellite sounding instruments, such as the Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS). However, this retrieval approach can also be applied to the ultraspectral sounding instruments to fly on polar satellites, such as the Infrared Atmospheric Sounding Interferometer (IASI) on the European MetOp satellite, the Cross-track Infrared Sounder (CrIS) on the NPOESS Preparatory Project, and the follow-on NPOESS series of satellites.

Abstract. NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in the timeframe of 2023, will carry a hyperspectral scanning radiometer named the Ocean Color Instrument (OCI) and two multi-angle polarimeters (MAPs): the UMBC Hyper-Angular Rainbow Polarimeter (HARP2) and the SRON Spectro-Polarimeter for Planetary EXploration one (SPEXone). The MAP measurements contain rich information on the microphysical properties of aerosols and hydrosols and therefore can be used to retrieve accurate aerosol properties for complex atmosphere and ocean systems. Most polarimetric aerosol retrieval algorithms utilize vector radiative transfer models iteratively in an optimization approach, which leads to high computational costs that limit their usage in the operational processing of large data volumes acquired by the MAP imagers. In this work, we propose a deep neural network (NN) forward model to represent the radiative transfer simulation of coupled atmosphere and ocean systems for applications to the HARP2 instrument and its predecessors. Through the evaluation of synthetic datasets for AirHARP (airborne version of HARP2), the NN model achieves a numerical accuracy smaller than the instrument uncertainties, with a running time of 0.01 s in a single CPU core or 1 ms in a GPU. Using the NN as a forward model, we built an efficient joint aerosol and ocean color retrieval algorithm called FastMAPOL, evolved from the well-validated Multi-Angular Polarimetric Ocean coLor (MAPOL) algorithm. Retrievals of aerosol properties and water-leaving signals were conducted on both the synthetic data and the AirHARP field measurements from the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign in 2017. From the validation with the synthetic data and the collocated High Spectral Resolution Lidar (HSRL) aerosol products, we demonstrated that the aerosol microphysical properties and water-leaving signals can be retrieved efficiently and within acceptable error. Comparing to the retrieval speed using a conventional radiative transfer forward model, the computational acceleration is 103 times faster with CPU or 104 times with GPU processors. The FastMAPOL algorithm can be used to operationally process the large volume of polarimetric data acquired by PACE and other future Earth-observing satellite missions with similar capabilities.

Abstract. Fraction of absorbed Photosynthetically Active Radiation (FPAR) is one of the pivotal parameters in terrestrial ecosystem modelling and crop growth monitoring. Airborne LiDAR is an advanced active remote sensing technology which can acquire fine three-dimensional canopy structural information quickly and accurately. Although some previous studies have shown that LiDAR-derived metrics had strong relationships with canopy FPARs, these estimation models without physical meaning are hard to be extended to various vegetation canopies and different growth periods. This study proposed a physical FPAR inversion method based on airborne LiDAR data and field measurements. The method considered direct and diffuse radiations separately based on the SAIL model and energy budget balance principle. The canopy FPAR was inversed from the structural information provided by LiDAR point cloud data and the spectral information provided by ground measurements. The estimated FPAR was validated with the field-measured FPAR over 39 maize plots. Results showed that the proposed method had a good performance in estimating the total FPAR of maize canopy (R2 = 0.76, RMSE = 0.062, n = 39). This study provides the potential to estimate the total, direct, and diffuse FPARs of vegetation canopy from airborne LiDAR data.

For high-precision measurements of the CO2 column concentration in the atmosphere with airborne integrated path differential absorption (IPDA) Lidar, the exact distance of the Lidar beam to the scattering surface, that is, the length of the column, must be measured accurately. For the high-precision inversion of the column length, we propose a set of methods on the basis of the actual conditions, including autocorrelation detection, adaptive filtering, Gaussian decomposition, and optimized Levenberg–Marquardt fitting based on the generalized Gaussian distribution. Then, based on the information of a pair of laser pulses, we use the direct adjustment method of unequal precision to eliminate the error in the distance measurement. Further, the effect of atmospheric delay on distance measurements is considered, leading to further correction of the inversion results. At last, an airborne experiment was carried out in a sea area near Qinhuangdao, China on 14 March 2019. The results showed that the ranging accuracy can reach 0.9066 m, which achieved an excellent ranging accuracy on 1.57-μm IPDA Lidar and met the requirement for high-precision CO2 column length inversion.

Abstract. The observation of deep and optically thick aerosol layers by a nadir-pointing lidar poses a challenge in terms of the signal inversion into a geophysically meaningful quantity such as extinction coefficient. A far-end reference molecular layer will usually be unavailable if the observed layer is near the surface, and using a near-end reference results in an unstable mathematical solution. In this paper, it is demonstrated that a far-end reference, taken within the aerosol layer, yields a better solution, and that the influence of the reference reduces strongly when coming inward, so that 1–2 km above reference the solution can be trusted. A method is developed to set the reference using the assumption of a well-mixed layer near the surface, and its effect is tested on data collected during recent aircraft-based campaigns. The method is also tested on simulated profiles in order to verify its limits and accuracy. The assumption of a well-mixed layer can be relaxed if one is able to set the reference well within a layer rather than at its boundaries.

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.

L'impact des nuages de glace sur le cycle de l'eau et le bilan radiatif est encore incertain en raison de la complexité des processus nuageux qui rend difficile l'acquisition d'observations adéquates sur les propriétés des nuages de glace et leur représentation dans les modèles de circulation générale. Les instruments de télédétection actifs et passifs, tels que les radiomètres, les radars et les lidars, sont couramment utilisés pour les étudier. La restitution des propriétés microphysiques des nuages (extinction, contenu en glace, rayon effectif, ...) peut être effectuée à partir d'un seul instrument ou de la combinaison de plusieurs instruments. L’intérêt de l’utilisation de synergies instrumentales pour restituer les propriétés nuageuses réside dans le fait que cela permet de réduire les incertitudes dues aux lacunes des différents instruments pris séparément. La constellation de satellites A-Train a considérablement amélioré notre connaissance des nuages. Depuis 2006, le lidar à rétrodiffusion visible CALIOP embarqué à bord du satellite CALIPSO et le radar nuage à 94GHz CPR embarqué à bord du satellite CloudSat ont permis l’acquisition de profils nuageux sur l’ensemble du globe et de nombreuses méthodes synergiques de restitution ont été adaptées à ces instruments. En 2021 sera lancé un nouveau satellite, EarthCARE, embarquant des instruments de télédétection de pointe, notamment ATLID, un lidar à haute résolution spectrale (HSRL) à 355 nm et un radar nuage Doppler à 94 GHz. La mission principale de ce satellite est de quantifier les interactions entre les nuages, les aérosols et le bilan radiatif de la Terre afin d'améliorer les prévisions météorologiques et des modèles climatiques. Grâce à son instrumentation avancée installée sur une plate-forme unique, cette nouvelle mission devrait fournir des observations sans précédent des nuages depuis l'espace. Cependant, pour ce faire, les algorithmes synergiques développés pour les mesures de l'A-Train doivent être adaptés à cette nouvelle configuration instrumentale. Au cours de ma thèse, je me suis concentrée sur l'algorithme Varcloud développé en 2007 par Delanoë et Hogan et basé sur une technique variationnelle. La première partie du travail a consisté à adapter certains paramètres du modèle microphysique de l’algorithme aux études récentes d’une large base de données in situ. En particulier, les questions de la paramétrisation du rapport lidar et du choix de la relation masse-diamètre pour les cristaux de glace ont été abordées. La deuxième partie de mon travail a consisté à adapter l'algorithme de restitution Varcloud aux plates-formes aéroportées. Les plates-formes aéroportées sont idéales pour préparer et valider les missions spatiales, permettant de réaliser des mesures sous-trace, colocalisées avec les instruments spatiaux. En particulier, le HALO allemand et le Falcon 20 français ont des charges utiles très complémentaires et sont parfaitement conçus pour la préparation et la validation de la mission EarthCare. Les deux avions embarquent notamment un lidar à haute résolution spectrale (355 nm sur le Falcon et 532 nm sur le HALO) et un radar Doppler à 36 GHz (HALO) et 95 GHz (Falcon). À l'automne 2016, une campagne aéroportée dans laquelle les deux avions étaient impliqués s'est déroulée en Islande, à Keflavik, dans le cadre du projet NAWDEX. Les mesures recueillies au cours de cette campagne fournissent un ensemble de données intéressant pour caractériser la microphysique et la dynamique des nuages dans l'Atlantique Nord, région qui présent un grand intérêt pour les missions Cloudsat-CALIPSO et EarthCARE. En outre, une série de vols communs avec observation de la même scène nuageuse par les deux plates-formes ont été réalisées, fournissant des données permettant d'étudier l'influence de la configuration instrumentale sur les propriétés des nuages de glace restituées
The impact of ice clouds on the water cycle and radiative budget is still uncertain due to the complexity of cloud processes that makes it difficult to acquire adequate observations of ice cloud properties and parameterize them into General Circulation Models. Passive and active remote sensing instruments, radiometers, radars and lidars, are commonly used to study ice clouds. Inferring cloud microphysical properties (extinction, ice water content, effective radius, ...) can be done from one instrument only, or from the synergy of several. The interest of using instrumental synergies to retrieve cloud properties is that it can reduce the uncertainties due to the shortcomings of the different instruments taken separately. The A-Train constellation of satellites has considerably improved our knowledge of clouds. Since 2006, the 532nm backscattering lidar CALIOP on board the satellite CALIPSO and the 94GHz cloud radar CPR on board the satellite CloudSat have acquired cloud vertical profiles globally and many lidar-radar synergetic methods have been adapted to CloudSat and CALIPSO data. In 2021 will be launched a new satellite, EarthCARE, boarding state of the art remote sensing instrumentation, in particular ATLID, a High Spectral Resolution Lidar (HSRL) at 355nm and a Doppler cloud radar at 94 GHz. The main mission of this satellite is to quantify interactions between clouds, aerosols and the Earth's radiation budget in order to improve weather prediction and climate models. Thanks to its advanced instrumentation mounted on a single platform, this new mission is expected to provide unprecedented observations of clouds from space. However, to do so, the synergistic algorithms that were developed for A-Train measurements have to be adapted to this new instrumental configuration. During my PhD, I focused on the Varcloud algorithm that was developed in 2007 by Delanoë and Hogan, based on a variational technique. The first part of the work consisted in adapting some parameters of the microphysical model of the algorithm to recent studies of a large dataset of in-situ measurements. In particular, the questions of a parameterization of the lidar extinction-to-backscatter ratio and the choice of the mass-size relationship for ice crystals were addressed. The second part of my work consisted in adapting the Varcloud retrieval algorithm to airborne platforms. Airborne platforms are ideal to prepare and validate space missions, allowing for direct underpasses of spaceborne instruments. Moreover, German and French aircraft, respectively HALO and French Falcon 20 have very complementary payloads and are perfectly designed for the preparation, the calibration and the validation of EarthCare. Both aircraft board a high spectral resolution lidar (355 nm on the French Falcon and 532 nm on the HALO) and a Doppler radar at 36 GHz (HALO) and 95 GHz (Falcon). In fall 2016 a field campaign related to the NAWDEX project took place in Iceland, Keflavik with both aircraft involved. The measurements collected during this campaign provide an interesting dataset to characterize cloud microphysics and dynamics in the North Atlantic, which are of high interest regarding the Cloudsat-CALIPSO and EarthCARE missions. In addition, a series of common legs with the same cloud scene observed by both platforms were performed, providing data to study the influence of the instrumental configuration on the retrieved ice cloud properties

碩士
國立成功大學
測量及空間資訊學系
105
SUMMARY Until now, many approaches on 3D building modeling have been proposed. Generally, these methods are distinguished into two categories: data-driven approach and model-driven approach. The former one is based on the geometric information of the input data while the latter one is generally depended on the combination of pre-defined parametric primitives. Both of these two methods have disadvantages in modeling precision and flexibility. To achieve an appropriate balance between reconstruction precision and visualization aspects, this study proposes a novel method that utilizes block structures and Boolean operation to reconstruct building model. By using the proposed method, the reconstructed model maintains high complexity, flexibility and better precision. Keywords: Airborne LiDAR point cloud, Point cloud segmentation, Model refinement, Constructive Solid Geometry, Building model reconstruction