Mapping Nearshore Bathymetry with Spaceborne Data Fusion and State Space Modeling

Despite numerous techniques for measuring and estimating water depth, bathymetry in the nearshore zone is notoriously difficult to map. Dangerous sea states, noisy environmental conditions, and expensive survey operations, particularly in remote areas, contribute to the difficulties of obtaining data along the coast. Global datasets, derived mainly from satellite altimetry methods, do exist, but they have significant limitations nearshore. Numerous high-resolution datasets, conventionally acquired with acoustic and lidar techniques, also exist, but they cover only a small percentage of the world's coasts. Spaceborne data fusion employing multispectral satellite derived bathymetry (SDB) offers the potential to significantly reduce the global lack of nearshore bathymetry, coined the "white ribbon" by the hydrographic community, referring to the alongshore data gap on many nautical charts. A broad term, multispectral SDB spans a diverse spectrum of methods that have been used extensively in specific case studies, but the application of multispectral SDB on a global or regional scale is significantly limited by the availability of in situ reference depths needed to tune derived values. Additionally, many existing approaches only use a single multispectral image, which can result in significant errors or missing data if the image contains environmental or sensor noise, such as clouds, sediment plumes, or detector-edge artifacts. This dissertation presents two spaceborne empirical multispectral SDB methods to address shortcomings of existing SDB approaches and reduce the global shortage of nearshore bathymetry – (1) active/passive spaceborne data fusion combining MABEL/ICESat-2 and multispectral data and (2) state space modeling of Sentinel-2 and Landsat 8 multispectral data to generate gap-free models of relative SDB (rSDB) with corresponding uncertainty estimates. The recently launched ICESat-2 mission offers an opportunity for a completely spaceborne active-passive data fusion approach to nearshore bathymetry by potentially providing a global source of nearshore reference depths to tune empirical multispectral SDB algorithms. The main objectives of the ICESat-2 mission are to measure ice-sheet elevations, sea-ice thickness, and global biomass, but ICESat-2’s 532-nm wavelength photon-counting Advanced Topographic Laser Altimeter System (ATLAS) was first posited, then demonstrated capable of detecting bathymetry in certain nearshore environments. Presented in two studies conducted prior to ICESat-2’s launch, the active-passive approach is demonstrated with data from MABEL, NASA’s high-altitude ATLAS simulator system. The first study assessed the ability to derive bathymetry from MABEL and then evaluated the accuracy and reliability of MABEL bathymetry using data acquired in Keweenaw Bay, Lake Superior. The study also developed and verified a baseline model to predict numbers of bottom returns as a function of water depth. The second study completed the demonstration of the spaceborne active/passive data fusion method by synergistically fusing MABEL-derived bathymetry and Landsat 8 multispectral Operational Land Imager (OLI) imagery over the entire Keweenaw Bay study site using the Stumpf band-ratio algorithm. The study also assessed the spatiotemporal viability of the data fusion approach by characterizing the variability of global coastal water clarity as interpreted from Visible Infrared Imaging Radiometer Suite (VIIRS) Kd(490) data. The calculated SDB agreed with a high-resolution topobathymetric lidar dataset to within an RMSE of 0.7 m, and the spatiotemporal viability analysis indicated that the spaceborne active-passive data fusion approach may be viable over many regions of the globe throughout the course of a year. State space modeling of empirical multitemporal SDB overcomes limitations of single-image SDB by leveraging the bathymetric signal in multispectral time series to create gap-free models of relative SDB (rSDB) for an arbitrary date, enabling SDB for dates with noisy or no data. State space models (SSMs) are well established in many applications but are absent in empirical SDB literature. Consisting of a state equation, which relates consecutive state vectors, and an observation equation, which relates observations to the state vector, SSMs are typically solved using Kalman filtering techniques, which provide estimates of uncertainties along with state estimates. SSMs also provide a mechanism for data fusion by allowing an observation equation for multiple observed time series. The third study demonstrates a state space approach to empirical multispectral SDB by applying local level SSMs to Landsat 8 OLI and Sentinel-2 MSI rSDB time series, both separately and fused. A representative single-sensor SSM (Landsat 8) was transformed to SDB that agreed with a high-resolution topobathymetric lidar dataset to within an RMSE of 0.29 m, which indicates the promising performance of the state space framework. Internally consistent fused-sensor SSMs verified that state space modeling also offers a data-fusion method capable of incorporating time series from a diverse suite of multispectral sensors

Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

LiDAR technology is finding uses in the forest sector, not only for surveys in producing forests but also as a tool to gain a deeper understanding of the importance of the three-dimensional component of forest environments. Developments of platforms and sensors in the last decades have highlighted the capacity of this technology to catch relevant details, even at finer scales. This drives its usage towards more ecological topics and applications for forest management. In recent years, nature protection policies have been focusing on deadwood as a key element for the health of forest ecosystems and wide-scale assessments are necessary for the planning process on a landscape scale. Initial studies showed promising results in the identification of bigger deadwood components (e.g., snags, logs, stumps), employing data not specifically collected for the purpose. Nevertheless, many efforts should still be made to transfer the available methodologies to an operational level. Newly available platforms (e.g., Mobile Laser Scanner) and sensors (e.g., Multispectral Laser Scanner) might provide new opportunities for this field of study in the near future

Etude de la canopée forestière : De la mesure lidar aéroportée à l'observation spatiale

The research presented in this thesis aims at evaluating the usage of active remote sensor lidar, from space platforms to monitor forest systems at a global scale. Forest is one of the main climate regulators through biogeochemical cycles, in which the most important processes are water cycle and carbon cycle. However, it is still poorly documented at a global scale because it is not always easy to get the information. Airborne and spaceborne observations, through remote sensing, are therefore the most suitable approaches to characterize the interactions between forest and atmosphere, at a regional or global scale. In order to improve the lidar remote sensing, it was necessary to build a representative database of different forest types, from managed forest to primary tropical forest. These data are obtained from several experiments using a new airborne demonstrator, the ULICE (Ultraviolet LIdar for Canopy Experiment). These data allows us to evaluate different uncertainty sources associated with the measurements for the extraction of corresponding forest parameters (i.e. tree height, aboveground biomass, leaf area index, etc.). This evaluation is based on the development of a numerical simulator which takes into account the surface characteristics, the atmosphere, the lidar instrumentation, and the satellite orbitography. We show that the active remote sensing sensor lidar is a powerful measuring method to characterize the forest from airborne observations; it remains very promising for spaceborne systems in low orbit, between 300 and 500 km, as the International Space Station.Le travail de recherche présenté dans cette thèse a pour objectif principal l’évaluation de l’intérêt de la télédétection active par lidar, à partir de plateformes spatiales, pour le suivi des systèmes forestiers à l’échelle de la planète. La forêt est l’un des principaux modérateurs du climat par son action sur les grands cycles biogéochimiques dont les plus importants sont les cycles de l’eau et du gaz carbonique. Cet environnement est néanmoins très mal documenté à l’échelle globale car il n’est pas toujours facile d’accès. Les observations aéroportées et spatiales, par télédétection, sont donc les approches les mieux adaptées afin de donner accès aux différentes échelles caractéristiques des processus d’interaction entre le milieu atmosphérique et la forêt. Afin de valoriser la télédétection par lidar, il a été nécessaire de construire une base de données représentative de différents environnements forestiers, allant de la forêt gérée à la forêt tropicale primaire. Ces données ont été obtenues suite à plusieurs campagnes utilisant un nouveau démonstrateur lidar, le système ULICE (Ultraviolet LIdar for Canopy Experiment). Elles ont permis d’évaluer les différentes sources d’incertitudes liées à la mesure pour l’extraction des paramètres forestiers pertinents (i.e. hauteur des arbres, quantité de biomasse aérienne, indice de couverture foliaire, …). Cette évaluation a été possible suite au développement d’un simulateur numérique qui prend en compte les caractéristiques de la surface, de l’atmosphère, de l’instrumentation lidar et d’orbitographie du satellite. On montre que la télédétection active lidar est une méthode de mesure performante pour la caractérisation des forêts à partir d’observations aéroportées ; elle reste très attractive pour des systèmes spatiaux en orbite basse, entre 300 et 500 km, comme la station spatiale internationale

The global tree carrying capacity (keynote)

editorial reviewe

Improved estimates of vegetation and terrain parameters from waveform LiDAR.

Light Detection And Ranging (LiDAR) technologies have evolved rapidly over the last decade, contributing to our knowledge of the Earth's surface evolution from local to global scales. A relatively young form of LiDAR is continuous waveform, which has not yet been fully exploited. The current research investigates and develops new methods, highlighting the potential and possible pitfalls of working with continuous waveform LiDAR. The first piece of research investigates the effects of shadowing in LiDAR waveforms in physically observed, large footprint LiDAR waveforms, based on previous works noting shadowing effects in radiative transfer models, and in a controlled environment experiment. For this investigation airborne LiDAR derived digital elevation models were employed in conjunction with spatially corresponding waveform returns to identify possible shadowing effects. It was found that shadows occur more frequently over more severely sloped terrain, affecting the accuracy of waveform derived vegetation parameters. The implications of shadows in waveform data are also discussed. The second piece of research develops and tests two methods, the Slope Screening Model and Independent Slope Model, such to determine ground slope information from LiDAR waveforms. Both methods were validated against discrete return airborne LiDAR data, and British Ordnance Survey data, such to identify winch method is most suited to retrieving slope. The third piece of research utilises the favoured method for slope prediction from the second r(\searc4i topic to correct vegetation height estimates for slope. Two methods (Lox' and modified) are investigated and tested, and validated against airborne LiDAR equivalent results at the regional scale, and against normalised difference vegetation index at the near global scale. Both correction methods produced statistically significant differences in mean global vegetation heights with regards to a control dataset

Remote Sensing of Biophysical Parameters

Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security)

ESTIMATING SURFACE ELEVATION BIASES FROM SUBSURFACE SCATTERED PHOTONS FOR LASER ALTIMETERS

Three decades of satellite observations have revealed rapid changes in Earth’s cryosphere associated with anthropogenic climate change, including decreased extent and volume of Arctic sea ice, mass loss from the Greenland Ice Sheet, mass loss in West Antarctica and the Antarctic Peninsula, and increased outlet glacier discharge in Greenland and Antarctica. NASA’s ICESat-2 mission will continue observing these rapid changes by measuring individual photons’ round-trip travel times from the satellite to Earth’s surface, providing precise estimates of surface elevation, and subsequent mass change for ice sheets and sea ice freeboard in Earth’s polar regions. This study investigates the potential bias in ICESat-2 surface elevation estimates from photons that have volume scattered in snow by: (1) measuring the transmission of green light through snow, (2) developing a method capable of characterizing the effects of volume scattered photons recorded by laser altimeters, (3) applying this method to laboratory measurements of volume scattered photons using the simulation laser altimeter for ICESat-2, and (4) simulating volume scattered photon rage biases using a photon tracking Monte Carlo model. Transmission measurements show that green light attenuates by one order of magnitude every centimeter in the first four centimeters of snow, suggesting that detecting volume scattered photons originating from laser altimeters is unlikely after photons travel more than a few centimeters in snow. Laboratory measurements using ICESat-2’s simulation laser altimeter MABEL (Multiple Altimeter Beam Experimental Lidar), show volume scattered photon return biases of 5 – 10 cm. However, these laboratory measurements revealed a previously unidentified drift in MABEL’s ranging on the order of 5 cm, potentially overestimating the volume scattering bias. Simulations from a single-photon tracking Monte Carlo model developed for this study reveal that approximately 95% of backscattered photons accrue path lengths less than 5 cm. This suggests that while statistically possible for photons to accrue large path lengths, the likelihood of laser altimeters detecting these photons is small. The results from this work demonstrate that volume scattered photons may be measured by photon counting laser altimeters, but will produce little bias in derived elevation estimates due to their low frequency of measurement

Modélisation 3D du transfert raidatif pour simuler les images et données de spectroradiomètres et Lidars satellites et aéroportés de couverts végétaux et urbains

Les mesures de télédétection (MT) dépendent de l'interaction du rayonnement avec les paysages terrestres et l'atmosphère ainsi que des configurations instrumentales (bande spectrale, résolution spatiale, champ de vue: FOV,...) et expérimentales (structure et propriétés optiques du paysage et atmosphère,...). L'évolution rapide des techniques de télédétection requiert des outils appropriés pour valider leurs principes et améliorer l'emploi des MT. Les modèles de transfert radiatif (RTM) simulent des quantités (fonctions de distribution de la réflectance (BRDF) et température (BTDF), forme d'onde LiDAR, etc.) plus ou moins proches des MT. Ils constituent l'outil de référence pour simuler les MT, pour diverses applications : préparation et validation des systèmes d'observation, inversion de MT,... DART (Discrete Anisotropic Radiative Transfer) est reconnu comme le RTM le plus complet et efficace. J'ai encore nettement amélioré son réalisme via les travaux de modélisation indiqués ci-dessous. 1. Discrétisation de l'espace des directions de propagation des rayons. DART simule la propagation des rayons dans les paysages terrestres et l'atmosphère selon des directions discrètes. Les méthodes classiques définissent mal le centroïde et forme des angles solides de ces directions, si bien que le principe de conservation de l'énergie n'est pas vérifié et que l'obtention de résultats précis exige un grand nombre de directions. Pour résoudre ce problème, j'ai conçu une méthode originale qui crée des directions discrètes de formes définies. 2. Simulation d'images de spectroradiomètre avec FOV fini (caméra, pushbroom,...). Les RTMs sont de type "pixel" ou "image". Un modèle "pixel" calcule une quantité unique (BRDF, BTDF) de toute la scène simulée via sa description globale (indice foliaire, fraction d'ombre,...). Un modèle "image" donne une distribution spatiale de quantités (BRDF,...) par projection orthographique des rayons sur un plan image. Tous les RTMs supposent une acquisition monodirectionnelle (FOV nul), ce qui peut être très imprécis. Pour pouvoir simuler des capteurs à FOV fini (caméra, pushbroom,...), j'ai conçu un modèle original de suivi de rayons convergents avec projection perspective. 3. Simulation de données LiDAR. Beaucoup de RTMs simulent le signal LiDAR de manière rapide mais imprécise (paysage très simplifié, pas de diffusions multiples,...) ou de manière précis mais avec de très grands temps de calcul (e.g., modèles Monte-Carlo: MC). DART emploie une méthode "quasi-MC" originale, à la fois précise et rapide, adaptée à toute configuration instrumentale (altitude de la plateforme, attitude du LiDAR, taille de l'empreinte,...). Les acquisitions multi-impulsions LiDAR (satellite, avion, terrestre) sont simulées pour toute configuration (position du LiDAR, trajectoire de la plateforme,...). Elles sont converties dans un format industriel pour être traitées par des logiciels dédiés. Un post-traitement convertit les formes d'onde LiDAR simulées en données LiDAR de comptage de photons. 4. Bruit solaire et fusion de données LiDAR et d'images de spectroradiomètre. DART peut combiner des simulations de LiDAR multi-impulsions et d'image de spectro-radiomètre (capteur hyperspectral,...). C'est une configuration à 2 sources (soleil, laser LiDAR) et 1 capteur (télescope du LiDAR). Les régions mesurées par le LiDAR, dans le plan image du sol, sont segmentées dans l'image du spectro-radiomètre, elle aussi projetée sur le plan image du sol. Deux applications sont présentées : bruit solaire dans le signal LiDAR, et fusion de données LiDAR et d'images de spectro-radiomètre. Des configurations d'acquisition (trajectoire de plateforme, angle de vue par pixel du spectro-radiomètre et par impulsion LiDAR) peuvent être importées pour encore améliorer le réalisme des MT simulées, De plus, j'ai introduit la parallélisation multi-thread, ce qui accélère beaucoup les calculsRemote Sensing (RS) data depend on radiation interaction in Earth landscapes and atmosphere, and also on instrumental (spectral band, spatial resolution, field of view (FOV),...) and experimental (landscape/atmosphere architecture and optical properties,...) conditions. Fast developments in RS techniques require appropriate tools for validating their working principles and improving RS operational use. Radiative Transfer Models (RTM) simulate quantities (bidirectional reflectance; BRDF, directional brightness temperature: BTDF, LiDAR waveform...) that aim to approximate actual RS data. Hence, they are celebrated tools to simulate RS data for many applications: preparation and validation of RS systems, inversion of RS data... Discrete Anisotropic Radiative Transfer (DART) model is recognized as the most complete and efficient RTM. During my PhD work, I further improved its modeling in terms of accuracy and functionalities through the modeling work mentioned below. 1. Discretizing the space of radiation propagation directions.DART simulates radiation propagation along a finite number of directions in Earth/atmosphere scenes. Classical methods do not define accurately the solid angle centroids and geometric shapes of these directions, which results in non-conservative energy or imprecise modeling if few directions are used. I solved this problem by developing a novel method that creates discrete directions with well-defined shapes. 2. Simulating images of spectroradiometers with finite FOV.Existing RTMs are pixel- or image-level models. Pixel-level models use abstract landscape (scene) description (leaf area index, overall fraction of shadows,...) to calculate quantities (BRDF, BTDF,...) for the whole scene. Image-level models generate scene radiance, BRDF or BTDF images, with orthographic projection of rays that exit the scene onto an image plane. All models neglect the multi-directional acquisition in the sensor finite FOV, which is unrealistic. Hence, I implemented a sensor-level model, called converging tracking and perspective projection (CTPP), to simulate camera and cross-track sensor images, by coupling DART with classical perspective and parallel-perspective projection. 3. Simulating LiDAR data.Many RTMs simulate LiDAR waveform, but results are inaccurate (abstract scene description, account of first-order scattering only...) or require tremendous computation time for obtaining accurate results (e.g., Monte-Carlo (MC) models). With a novel quasi-MC method, DART can provide accurate results with fast processing speed, for any instrumental configuration (platform altitude, LiDAR orientation, footprint size...). It simulates satellite, airborne and terrestrial multi-pulse laser data for realistic configurations (LiDAR position, platform trajectory, scan angle range...). These data can be converted into industrial LiDAR format for being processed by LiDAR processing software. A post-processing method converts LiDAR waveform into photon counting LiDAR data, through modeling single photon detector acquisition. 4. In-flight Fusion of LiDAR and imaging spectroscopy.DART can combine multi-pulse LiDAR and cross-track imaging spectroscopy (hyperspectral sensor...). It is a 2 sources (sun, LiDAR laser) and 1 sensor (LiDAR telescope) system. First, a LiDAR multi-pulse acquisition and a sun-induced spectro-radiometer radiance image are simulated. Then, the LiDAR FOV regions projected onto the ground image plane are segmented in the spectro-radiometer image, which is also projected on the ground image plane. I applied it to simulate solar noise in LiDAR signal, and to the fusion of LiDAR data and spectro-radiometer images. To further improve accuracy when simulating actual LiDAR and spectro-radiometer, DART can also import actual acquisition configuration (platform trajectory, view angle per spectro-radiometer pixel / LiDAR pulse). Moreover, I introduced multi-thread parallelization, which greatly accelerates DART simulation

Advanced photon counting techniques for long-range depth imaging

The Time-Correlated Single-Photon Counting (TCSPC) technique has emerged as a candidate approach for Light Detection and Ranging (LiDAR) and active depth imaging applications. The work of this Thesis concentrates on the development and investigation of functional TCSPC-based long-range scanning time-of-flight (TOF) depth imaging systems. Although these systems have several different configurations and functions, all can facilitate depth profiling of remote targets at low light levels and with good surface-to-surface depth resolution. Firstly, a Superconducting Nanowire Single-Photon Detector (SNSPD) and an InGaAs/InP Single-Photon Avalanche Diode (SPAD) module were employed for developing kilometre-range TOF depth imaging systems at wavelengths of ~1550 nm. Secondly, a TOF depth imaging system at a wavelength of 817 nm that incorporated a Complementary Metal-Oxide-Semiconductor (CMOS) 32×32 Si-SPAD detector array was developed. This system was used with structured illumination to examine the potential for covert, eye-safe and high-speed depth imaging. In order to improve the light coupling efficiency onto the detectors, the arrayed CMOS Si-SPAD detector chips were integrated with microlens arrays using flip-chip bonding technology. This approach led to the improvement in the fill factor by up to a factor of 15. Thirdly, a multispectral TCSPC-based full-waveform LiDAR system was developed using a tunable broadband pulsed supercontinuum laser source which can provide simultaneous multispectral illumination, at wavelengths of 531, 570, 670 and ~780 nm. The investigated multispectral reflectance data on a tree was used to provide the determination of physiological parameters as a function of the tree depth profile relating to biomass and foliage photosynthetic efficiency. Fourthly, depth images were estimated using spatial correlation techniques in order to reduce the aggregate number of photon required for depth reconstruction with low error. A depth imaging system was characterised and re-configured to reduce the effects of scintillation due to atmospheric turbulence. In addition, depth images were analysed in terms of spatial and depth resolution