Self-organizing nonlinear output (SONO): A neural network suitable for cloud patch-based rainfall estimation at small scales

Accurate measurement of rainfall distribution at various spatial and temporal scales is crucial for hydrological modeling and water resources management. In the literature of satellite rainfall estimation, many efforts have been made to calibrate a statistical relationship (including threshold, linear, or nonlinear) between cloud infrared (IR) brightness temperatures and surface rain rates (RR). In this study, an automated neural network for cloud patch-based rainfall estimation, entitled self-organizing nonlinear output (SONO) model, is developed to account for the high variability of cloud-rainfall processes at geostationary scales (i.e., 4 km and every 30 min). Instead of calibrating only one IR-RR function for all clouds the SONO classifies varied cloud patches into different clusters and then searches a nonlinear IR-RR mapping function for each cluster. This designed feature enables SONO to generate various rain rates at a given brightness temperature and variable rain/no-rain IR thresholds for different cloud types, which overcomes the one-to-one mapping limitation of a single statistical IR-RR function for the full spectrum of cloud-rainfall conditions. In addition, the computational and modeling strengths of neural network enable SONO to cope with the nonlinearity of cloud-rainfall relationships by fusing multisource data sets. Evaluated at various temporal and spatial scales, SONO shows improvements of estimation accuracy, both in rain intensity and in detection of rain/no-rain pixels. Further examination of the SONO adaptability demonstrates its potentiality as an operational satellite rainfall estimation system that uses the passive microwave rainfall observations from low-orbiting satellites to adjust the IR-based rainfall estimates at the resolution of geostationary satellites. Copyright 2005 by the American Geophysical Union

A Nonparametric Approach to Segmentation of Ladar Images

The advent of advanced laser radar (ladar) systems that record full-waveform signal data has inspired numerous inquisitions which aspire to extract additional, previously unavailable, information about the illuminated scene from the collected data. The quality of the information, however, is often related to the limitations of the ladar camera used to collect the data. This research project uses full-waveform analysis of ladar signals, and basic principles of optics, to propose a new formulation for an accepted signal model. A new waveform model taking into account backscatter reflectance is the key to overcoming specific deficiencies of the ladar camera at hand, namely the ability to discern pulse-spreading effects of elongated targets. A concert of non-parametric statistics and familiar image processing methods are used to calculate the orientation angle of the illuminated objects, and the deficiency of the hardware is circumvented. Segmentation of the various ladar images performed as part of the angle estimation, and this is shown to be a new and effective strategy for analyzing the output of the AFIT ladar camera

A proposed decision support tool for wood procurement planning based on stereo-matching of aerial images

In Sweden the cap portion of the harvested stem volume derives from Non-Industrial Private Forest (NIPF) owners. In the current study a Decision Support Tool (DST) for wood procurement planning based on stereo-matching of aerial images is presented. Two stages are described, namely (1) automatic segmentation using a Mean Shift algorithm; (2) wall-to-wall mapping of the stands using Semi-Global Matching (SGM) in combination with a high-resolution Digital Elevation Model (DEM). The study was conducted in a coniferous boreal forest area in northern Sweden. 365 sample plots (8 m radius) were measured in the field where HGV (dm) ranged between 49.0 - 246.0 dm (mean 139.3 dm), DGV 67.0 - 400.0 mm (mean 196.8 mm), VOL 7.0 - 665.0 m3/ha (mean 151.1 m3/ha) and BA 20.0 - 635.0 dm2/ha (mean 204.9 dm2/ha). Point clouds were extracted from the aerial images with 60% forward overlap. A canopy cover metric was used to improve the VOL and BA estimations. Plot level accuracies were calculated using leave-one-stand-out-cross-validation resulting in a Root Mean Square Error (in percent of surveyed mean) for: HGV 11.2%, DGV 15.2%, VOL (m3/ha) 33.5% and BA 30.3%. Each stand was given an average of the estimated forest variables enabling ranking between the stands based on their estimated values. The results indicated that the proposed DST can be used as a support in wood procurement planning. Aerial images are an appropriate data source in the proposed DST, mainly because of the readily availability and low cost

New developments in Stimulated Raman Scattering and applications to plastic particle detection in the environment and human tissue

This thesis deals with an advanced laser-based microscopy technique to detect micrometer-size objects with molecular specificity. Applications are shown from the aquatic environment and from the medical world. The final chapters describe an option to increase the penetration depth through scattering samples and simulation software to help optimize the measurement settings. One of the most prominent materials in modern life is plastic, but this also results in the large-scale production of plastic waste. A portion of this waste reaches the environment and is fragmented into small pieces, called microplastics. Microplastics pollution affects the environment and potentially our health in ways we are only beginning to understand. To study it, we need to have a solid measurement and monitoring platform, based on reliable microplastics detection. Detection of microplastics is difficult due to their small size and heterogeneity and they can be found in different types of matrices in the environment and even in the human body. A label-free microscopy imaging technique, called Stimulated Raman Scattering (SRS) microscopy, is able to create images of small particles, like microplastics, based on their molecular structure. SRS makes use of two synchronized pulsed lasers of different colors, of which the energy difference matches a specific vibration of the target molecule. In this thesis, we used SRS for identifying five polymer types. First, we tested the approach on an artificial mixture of plastic particles, and we identified polyethylene terephthalate particles extracted from nail polish, demonstrating also the thousand‐fold higher speed of mapping compared with conventional Raman. Furthermore, we found 12,000 plastic particles per kilogram dry weight in a Rhine estuary sediment sample. SRS was the fastest microplastics detection method at the time of publication. We concluded that SRS can be an efficient method for monitoring microplastics in the environment and potentially many other matrices of interest. Another application area that was studied with SRS is breast tissue from explanted breast implants. Implant failure occurs in approximately a tenth of patients within 10 years, and even without a major rupture silicone can still leak. We showed how SRS can detect silicone material in breast tissue slices, without additional sample treatment. SRS images revealed the distribution and quantity of silicone material. Twenty-two donor-matched capsules from eleven patients experiencing unilateral capsular contraction complaints were included in a clinical study after bilateral explantation surgery. This method showed the correlation between silicone presence and capsular contraction. Depth penetration of the light into the sample is an issue with any light based technique. We showed the use of a long wavelength SRS microscope system capable of greater depth imaging compared with the more common configuration with shorter wavelengths. It showed an improved depth penetration in polyethylene plastic material, in a silicone test sample with embedded polyethylene microbeads, and into subcutaneous fat tissue. In SRS imaging we have to consider multiple parameters that influence the imaging speed, image quality and the spatial resolution. In order to find the optimized imaging setup, we developed two simulation programs for SRS imaging systems with lock-in amplifier. One simulation program was used to find parameters optimized for either image quality or acquisition time. With the second program we evaluated SRS imaging; the simulations agreed very well with experimental SRS images. The same software was used to simulate multiplexed SRS imaging. of six channels, including the inter-channel crosstalk. These programs will be useful for operating an SRS imaging setup, as well as for designing novel setups

Monitoramento das dinâmicas espaciais e temporais dos fluxos sedimentares na Bacia Amazônica a partir de imagens de satélite

Tese (doutorado)—Universidade de Brasília, Instituto de Geociências, 2013.A bacia amazônica é a mais importante do mundo em termos de superfície e de vazão ao oceano, representando uns 15% do volume d’água chegando aos oceanos e proveniente dos continentes. O fluxo de sedimento do Rio Amazonas foi estimado entre 600 e 1200 Milliões de toneladas. Na bacia do Amazonas, por causa de seu tamanho e de difícil acesso, é demasiado complexo e dispendioso o monitoramento das sub-bacias importantes. Para resolver esses problemas serão exigidos instrumentos alternativos, como imagens de satélite. A principal desvantagem da utilização de sensores ópticos nesta região é a grande quantidade de nuvens, mas este problema pode ser solucionado com a utilização de imagens de alta resolução temporal. Este trabalho tem como principal objetivo controlar os fluxos de sedimentos nos grandes rios da Amazônia, e classificar os diferentes tipos de água presente na bacia, a partir de suas características ópticas. O sensor MODIS (Moderate Resolution Imaging Spectroradiometer), a bordo dos satélites TERRA e AQUA, fornecem imagens diárias por cada satélite, fazendo um total de 2 imagens por dia, com resolução espacial de até 250 m. Nesta pesquisa serão usadas imagens compostas de 8 dias com resolução espacial de 250 e 500 m para ambos os satélites, fazendo um total de 4 produtos MODIS (MOD09Q1, MOD09A1, MYD09Q1 e MYD09A1). Estes produtos fornecem dados de reflectância com uma correção atmosférica bastante robusta. A rede de monitoramento ORE-HIBAM vem coletando dados de qualidade de água, a cada dez dias, desde 2003, em diferentes locais da bacia amazônica. Estas amostras são para a medição da concentração de matéria em suspensão (MES) de superfície. Para calibrar estas estações, o projeto HIBAM, em cooperação com entidades regionais, realiza campanhas para a coleta de amostras d’água e medição de diferentes parâmetros. Nessas campanhas foram também realizadas medições de radiometria (reflectância de sensoriamento remoto (Rrs) e coeficiente de atenuação difusa (Kd)) da água. De maneira simultânea às medições de radiometria foram coletadas amostras de água. As medições de Rrs foram relacionados com as concentrações correspondentes de MES, obtendo um coeficiente de correlação (R2 ) de 0,89 para 259 amostras. Da mesma forma foram correlacionados com as medidas de Kd e a concentração de MES, obtendo R2 de 0,93 para 129 amostras. Ambas as medidas com um intervalo de concentrações de 2-621.6 mg/l. As medições de radiometria também foram utilizadas para classificar as águas naturais da Amazônia em 8 classes, dependendo de suas propriedades ópticas. Com as medidas radiométricas de campo foi possível calcular o coeficiente de absorção (a) das águas naturais, estimar a absorção do CDOM (aCDOM) e dos sedimentos (as). Usando estes dados, também foi calculado o coeficiente de espalhamento (b) e retroespalhamento (bb) para diferentes tipos de água, com resultados consistentes com a literatura. A extração da reflectância das imagens MODIS foi realizada de maneira automatizada, mediante a ferramenta computacional MOD3R. Esta ferramenta extrai a reflectância dos pixels correspondentes da água, a partir de uma região previamente designada. Desta forma, pode-se processar e analisar séries históricas a partir de uma grande quantidade de imagens (mais de 500 imagens por estação). Com as imagens MODIS foram criadas 6 estações virtuais ao longo do Rio Madeira. Para esse trabalho foi usada uma razão de bandas (infravermelha / vermelha) e, a partir dos dados de radiometria de campo e de concentração de MES das campanhas, os dados MODIS foram calibrados a partir da relação entre dados de MES e o resultado da razão de bandas. Os dados de MES estimados com as imagens MODIS foram validados com os dados das estações de Fazenda Vista Alegra e Porto Velho, da rede ORE-HIBAM obtendo-se um valor de R2 = 0.78. Assim, foram estimados os valores de MES para cada estação e para um período de 2000 a 2011. Com esses dados, foi calculado um ano médio (12 médias mensais) para as seis estações. Assim, observamos os processos de transporte de sedimentos como diluição e precipitação ao longo do rio Madeira e do comportamento temporal em cada estação e época do ano, e da influência nos sedimentos do remanso hidráulico causado pelo rio Amazonas, em alguns meses do ano. Na confluência dos rios Ucayali e Maranon é formado o rio Amazonas (peruano). Nessa região existem três estações de amostragem de concentração de sedimentos de superfície, da rede ORE-HYBAM, nos três rios (Marañón, Ucayali e Amazonas). A estação do rio Ucayali teve um problema causado pela pluma de um rio afluente, fazendo com que as amostras nesta estação representem as águas do afluente. O projeto HIBAM realiza campanhas de amostragem de sedimentos e medição do caudal sólido e líquido, assim podem se ligar as amostras de sedimentos de superfície com descarga sólida. Os dados de reflectância infravermelha MODIS foram relacionados com as concentrações médias de superfície, medidas durante as campanhas, obtendo-se boas correlações entre estas duas magnitudes. Usando as relações MES- Reflectância, foram estimadas series de concentração de sedimentos e posteriormente foi estimada a descarga sólida em 3 estações. Nas estações dos rios Marañon e Amazonas, os dados estimados com as imagens de satélite foram validadso com os dados da rede ORE-HIBAM. Para validar o resultado do rio Ucayali foi realizado um balanço de massa entre as três estações, de modo que a descarga sólida dos rios Maranon e Ucayali seja igual à descarga sólida na estação do rio Amazonas. O equilíbrio foi realizado com dados MODIS estimados, em uma série de imagens entre os anos 2000 e 2009, fechando o equilíbrio entre as estações tanto a montante quanto a jusante. Na presente pesquisa foram estimados dados de MES para um intervalo entre 4 e 1832 mg/l, sem achar saturação na reflectância do canal infravermelho, na razão de bandas e no Kd. As estimações de MES, a partir dos dados MODIS, realizadas na presente pesquisa mostraram um erro médio quadrático entre 30 e 40%. Com a utilização da radiometria de campo, este erro diminui cerca de 23%. ______________________________________________________________________________ RÉSUMÉLe bassin Amazonien est le grand plus réseau hydrographique du monde en termes d’extension géographique et de débit. Il couvre approximativement 5 % des surfaces émergées, représente 15 % des apports continentaux en eau douce aux océans tandis que son débit sédimentaire est de l’ordre de 800 millions de tonnes par an. Le suivi hydro-sédimentaire des fleuves amazoniens est rendu difficile par la taille du bassin et la puissance des flux à mesurer pour lesquels les méthodes traditionnelles de caractérisation sont peu adaptées. Les données de télédétection optique pourraient représenter une alternative intéressante pour le suivi de paramètres de qualité des eaux, notamment pour des grands bassins « sous » instrumentés comme l’Amazonie. Un obstacle important reste cependant le fort ennuagement typique des zones tropicales humides qui ne peut être dépassé que par l’utilisation d’une très haute résolution temporelle. L’objectif de la présente thèse est de caractériser les flux sédimentaires des principaux fleuves amazoniens à partir du suivi par télédétection des propriétés optiques de leurs eaux. Les capteurs MODIS (Moderate Resolution Imaging Spectroradiometer) à bord des satellites Terra et Aqua, fournissent des images journalières sur toute la surface terrestre. Nous considérons les produits continentaux composites à 8 jours et à 250 mètres de résolution spatiale. Ces images présentent l’avantage d’être calibrées, corrigées des effets atmosphériques et géoréférencées de manière robuste permettant un traitement automatisé de longues séries temporelles depuis l’an 2000. La caractérisation des flux sédimentaires in situ se base sur les données de réseaux conventionnels de mesure (ORE-HYBAM) et des campagnes de mesure qui ont permis de mesurer, selon des transects amont-aval, les principales caractéristiques des flux hydrologiques (débit, variations spatiales et saisonnières), des matières en suspension (MES) (concentration, minéralogie, granulométrie) et de leurs propriétés optiques (propriétés optiques apparentes AOP – réflectance télédétectée Rrs et coefficient d’atténuation diffus vertical descendant Kd). Un total de 279 mesures de Rrs et 133 de Kd sont analysées afin de déterminer la variabilité des propriétés optiques des MES au sein du bassin versant de l’Amazone et durant les différentes périodes du cycle hydrologique. Une classification non supervisée de Rrs permet de séparer aisément les eaux des plaines d’inondation et les grands types d’eaux fluviales (eaux noires / claires / blanches). La réflectance est bien corrélée avec la concentration en MES dans l’infrarouge (r² = 0.81 – 840 0.9), sans saturation et pour une large gamme de longueur d’ondes du vert (500 nm) à l’infrarouge (850 nm). Les propriétés optiques inhérentes (IOP) sont aussi étudiées directement (matière organique dissoute colorée – CDOM) ou déduites à partir des mesures des AOP. La moyenne de l'absorption du CDOM à 440 nm varie en fonction des types d’eaux. Pour les eaux noires, aCDOM est de 7.9 m-1, alors qu’il est de l’ordre de 4.8 m-1, pour les eaux blanches. La relation entre aNAP (coefficient d’absorption du matériel particulaire) à 550 nm et la MES est très robuste (r2 =0.91) mais présente une dispersion significative pour les faibles concentrations. L'absorption spécifique des particules non algales (a*NAP), qui est définie comme l'absorption par unité de concentration est évaluée à 0.028 m2/g à 555 nm. La variation de aNAP est modélisée par une exponentielle négative dont l’exposant varie entre 0.006 et 0.015 avec une corrélation négative avec la MES. Le coefficient de diffusion spécifique des particules non algales b*NAP à 555 nm est en moyenne de 0.672 ± 0.18 m2.g-1 et montre une variation spectrale du type λ-0.77 avec la longueur d’onde. Alors que sur l’Amazone et son principal affluent, le Solimões, aucunes variations saisonnières ne sont détectées, on mesure une variation saisonnière de b*NAP au sein du fleuve Madeira qui contribue à hauteur de 50% au débit solide du fleuve Amazone. L’utilisation des données satellitaires de résolution moyenne (hectométrique) est rendu difficile par l’étroitesse des cours d’eau vis-à-vis de la taille des pixels. Le phénomène de mélange spectral peut altérer la réflectance des pixels d’eau en fonction de la proximité d’éléments possédant des signatures spectrales contrastées (végétation de berge). Un algorithme a été développé afin d’identifier de manière automatique les pixels purs d’eaux au sein des scènes MODIS. La réflectance des eaux fluviales calculées par l’algorithme est validée avec les données radiométriques de terrain décrites précédemment, avec une bonne précision et avec un biais compatible avec les études de CAL/VAL précédemment publiées en milieu tropical humide marquée la présence d’aérosols en grande quantité. L’utilisation de cet algorithme permet un traitement automatisée des séries temporelles MODIS sur toutes les stations du réseau HYBAM en Amazonie et sans connaissance a priori des caractéristiques hydrologiques, météorologiques ou de la géométrie d’acquisition. Au Brésil, le fleuve Madeira est étudiée de manière systématique avec les données MODIS Terra et Aqua à partir de la création d’un réseau de stations virtuelles le long du cours d’eau. L’analyse conjointe des données satellitaires, radiométrique de terrain et des données de MES à deux stations (Porto Velho et Borba) met en évidence une hystérésis dans la relation Rrs – concentration en MES. En effet, il apparait que pour une même concentration en MES, la Rrs est inférieure en période de pic de crue, un comportement cohérent avec celui détecté pour le coefficient de diffusion spécifique de la MES comme décrit précédemment. Cette sensibilité est expliquée par une variation du type de MES qui affecte leur propriétés optique bien qu’il ne soit pas possible de conclure sur l’origine exacte de cette variation (variabilité granulométrique, minéralogique ou de la fraction organique). Cependant, l’utilisation d’un ratio Rrs(Infrarouge) / Rrs(Rouge) permet de s’affranchir de cette sensibilité saisonnière et permet un suivi précis de la concentration en MES comme l’atteste la validation avec les données du réseau HYBAM (r = 0.79 – N = 282) pour une large gamme de MES (4 – 1832 mg/ l). L´étude des comportements moyens de la concentration en MES mesurée par satellite au pas de temps mensuel (estimée par une moyenne interannuelle entre 2000 et 2011), d’amont en aval, permet le suivi fin des processus hydro-sédimentaires qui se développent au cours de la traversée du Madeira au sein de la plaine amazonienne jusqu’à sa confluence avec le fleuve Amazone : dilution, sédimentation et resuspension. En particulier, la zone de sédimentation induite par le barrage hydraulique à la confluence Madeira / Amazone est précisément délimitée lors de la période d’étiage. Au Pérou, nous étudions la zone de confluence Marañon / Ucayali où se forme le fleuve Amazone et où l’ORE HYBAM maintient une station hydro-sédimentaire sur chaque cours d’eau avec le service hydrologique péruvien. La station terrain de l’Ucayali montre des mesures incohérentes pendant plusieurs années du fait de l’influence d’un affluent local à l’amont de la station, rendant impossible l’utilisation de ces données. Pour cette étude, les images MODIS sont calibrées à partir de campagnes de mesures intensives des MES aux trois stations du réseau HYBAM entre 2007 et 2009. La validation est effectuée de manière indépendante de deux manières. D’abord en comparant les MES estimées par satellite et les données du réseau HYBAM (données à 10 jours) aux deux stations montrant des enregistrements valides (fleuves Amazone et Marañon). Ensuite, les données de MES de surface estimées par satellite sont utilisées pour calculer une concentration moyenne sur la colonne d’eau grâce aux données de campagne HYBAM. Les MES moyennes sur la section sont ensuite multipliées par le débit liquide pour calculer un débit sédimentaire à chaque station dans la zone de confluence. La comparaison des débits solides déduit par satellite entre amont (Marañon + Ucayali) et aval (Amazone) montre une excellente robustesse des estimations satellitaires (RMSE de 18 %, biais de 3 % sur 104 mois de données) compatible avec une utilisation opérationnelle des données MODIS pour le suivi des flux sédimentaires au sein du bassin amazonien. La présente thèse démontre pour la première fois que les propriétés optiques des MES au sein d’un grand bassin versant hydrologique sont suffisamment stables spatialement et temporellement afin de permettre un suivi efficace des flux sédimentaires de surface. Nous avons également démontré que les données MODIS, grâce au post-traitement que nous présentons, permettent de suivre robustement la réflectance des eaux de rivières. L’exploitation des images satellitaires permet ainsi de mettre en évidence les processus hydro-sédimentaires sur de larges périodes de temps (> 10 ans) et de mesurer les flux sédimentaires en conjonction avec les réseaux conventionnels de mesure. ______________________________________________________________________________ ABSTRACTThe Amazon basin is the largest hydrographical network in terms of geographical extension and discharge. It covers approximatively 5% of the continental surface, represents 15% of the fresh water continental contribution to the ocean, while its solid discharge is of around the 800 millions of ton per year. The hydro-sedimentary monitoring of the Amazonian rivers is limited by the large extension of the basin and the magnitude of the fluxes to measure, for which the traditionally characterisation methods are not well adapted. The optical remote sensing data could represent an interesting alternative for the monitoring of water quality parameters, particularly for large under-instrumented basins like the Amazon. However, a main limitation of this method is the typical nebulosity of the wet tropical regions. This difficulty can only be resolved by using very high temporal resolution of the data. The objective of this thesis is to characterize the sedimentary fluxes of the main amazonian rivers, using the remote sensing monitoring of their water optical properties. The MODIS sensors (Moderate Resolution Imaging Spectroradiometer) on board of Terra and Aqua satellites provide daily images on the whole Earth surface. The continental product composites every 8 days and with 250-m spatial resolution are used. The advantage of those images is that they are calibrated, the atmospheric effects are corrected, and they are robustly georeferenced, which make possible an automatized treatment of large temporal series since 2000. The in-situ sedimentary fluxes characterization is based on a conventional measurement network data (ORE-HYBAM) and field campaigns. Those data provided, via upstream-downstream section, main characteristics of hydrological fluxes (discharge, spatial and seasonal variabilities), suspended sediment (SS) (concentration, mineralogy and granulometry) and their optical properties (apparent optical proprieties AOP – remote sensing reflectance Rrs and downwelling diffuse attenuation coefficient Kd). A total of 279 measurements of Rrs and 133 of Kd are analyzed in order to determinate the variability of optical properties of SS into the Amazon basin and during the distinct periods of the hydrological cycle. With a classification not supervised of Rrs , the flood plains water and the main fluvial water types (black water/clear/white) are separated. The reflectance is well correlated with the SS concentration in the infrared (r² = 0.81 – 840 0.9), without saturation and for a large range of wavelength from green (500 nm) to infrared (850 nm). The inherent optical properties (IOP) are also directly studied (colored dissolved organic matter – CDOM) or deduced from AOP measurements. The mean absorption of the CDOM at 440 nm differs according to water types. For black water, aCDOM is 7.9 m-1, while it is of about 4.8 m-1 for white waters. The relation between aNAP (absorption coefficient of the suspended sediment) at 550 nm and the SS is robust (r2 =0.91) but shows a significative dispersion for weak concentrations. The specific absorption of the non- algal particles (a*NAP), which is defined as the absorption per concentration unity is estimated at 0.028 m2/g à 555 nm. The variation of aNAP is modelized by a negative exponential with an exponent that varies from 0.006 to 0.015, with a negative correlation with the SS. The scattering coefficient specific of the non-algal particles b*NAP at 555 nm is in average of 0.672 ± 0.18 m2.g-1 and shows a spectral variation of the -0.77 type with the wavelength. While for the Amazon and its main tributary, the Solimões, no seasonal variation are detected, a seasonal variation of b*NAP is measured for the Madeira river, which contribute in around 50% to the solid discharge of the Amazon mainstream. The utilization of the medium resolution satellital data (hectometric) is complicated due to the river narrowness by comparison to the pixel size. The mixing spectral phenomenon can degrade the reflectance of the water pixels, in relation to the proximity of the elements having contrasted spectral signatures (riverbank vegetation). An algorithm was developed in order to automatically identify the pure water pixels into the MODIS images. The fluvial water reflectance calculated with the algorithm is validated with the in-situ radiometric data previously described, with a good precision and a compatible bias with the CAL/VAL studies previously published in humid tropical environment characterized by the strong quantity of aerosols. This algorithm is used to automatically treat the MODIS temporal series over all the HYBAM network stations in the Amazon basin, and without an a priori knowledge of hydrological, meteorological or acquisition geometry characteritics. In Brazil, the Madeira River is systematically studied with the MODIS Terra and Aqua data from the creation of a virtual stations network along the river. The parallel analysis of the satellital, in-situ radiometric and SS data at two stations (Porto Velho and Borba) put in evidence an hysterisis in the relation Rrs – SS concentration. Indeed, it seems that for a similar SS concentration, the Rrs is lower during the highflow period, a coherent behavior, with regards to the one detected for the SS specific scattering coefficient, as previously described. This sensibility is explained by a variation of the SS type, which affect their optical properties, while it is not possible to conclude about the extract origin of this variation (granulometrical, mineralogical or organic fraction variability). However, the Rrs(Infrared) / Rrs(Red) ratio is used to avoid the seasonal sensibility and make possible the precise monitoring of the SS concentration, as the validation of HYBAM network data has demonstrated (r = 0.79 – N = 282) for a large SS range (4 – 1832 mg/ l). The study of the mean behaviors of the SS concentration measured by satellite with a monthly time step (estimated with a interannual me

Suivi de la dynamique spatiale et temporelle des flux sédimentaires dans le bassin de l'Amazone à partir d'images satellite

L'objectif de cette thèse est de caractériser les flux sédimentaires des principaux fleuves amazoniens à partir du suivi par télédétection des propriétés optiques de leurs eaux. Des campagnes ont permis de mesurer les principales caractéristiques des flux hydrologiques, des matières en suspension (MES) et de leurs propriétés optiques apparentes. La réflectance télédétectée est bien corrélée avec le MES dans l'infrarouge (r²=0.81-8400.9), entre les longueurs d'onde de 500 à 800nm. Les données satellitaires MODIS sont privilégiées dans cette étude du fait de leur fréquence d'acquisition. L'utilisation de ces images est rendue difficile par l'étroitesse des cours d'eau vis-à-vis de la taille des pixels. Un algorithme a été développé afin d'identifier de manière automatique les pixels d'eaux purs au sein des scènes MODIS. La réflectance des eaux fluviales calculées par l'algorithme est validée avec les données radiométriques, avec une bonne précision. L'utilisation de cet algorithme permet un traitement automatisée des séries temporelles MODIS. Les données satellites sont utilisées dans un bassin versant de l'Amazone au Pérou et celui du Madeira au Brésil pour vérifier la robustesse des estimations par satellite et comprendre la variabilité spatio temporelle des processus hydrosédimentaires La présente thèse démontre pour la première fois que les propriétés optiques des MES au sein d'un grand bassin versant hydrologique sont suffisamment stables spatialement et temporellement pour permettre un suivi des flux sédimentaires par tédétection.The objective of this thesis is to characterize the sedimentary fluxes of the main Amazonian rivers, using the remote sensing monitoring of their water optical properties. The field campaigns provided main characteristics of hydrological fluxes, suspended sediment (SS) and their apparent optical properties. Remote sensing reflectance is well correlated with the SS concentration in the infrared (r² = 0.81-840 0.9), without saturation between 500 - 850 nm. MODIS data was chosen in this study because of their high acquisition frequency. However, the use of such images is complicated because of the small size of the river steam in comparison to the pixel size. An algorithm was developed in order to automatically identify the pure water pixels into the MODIS images. The fluvial water reflectance calculated with the algorithm is validated with the in-situ radiometric data previously described, with a good precision. This algorithm is used to process automatically MODIS temporal series, along the Amazon River in Peru and the Madeira River in Brazil to check the quality of satellite estimates and understand the temporal and spatial variability of hydrosedimentary processes. This thesis demonstrates, for the first time, that the suspended sediment optical properties in a large watershed are spatially and temporally stable enough to allow effective monitoring of surface sediment flow using remote sensing

Remote Sensing in Mangroves

The book highlights recent advancements in the mapping and monitoring of mangrove forests using earth observation satellite data. New and historical satellite data and aerial photographs have been used to map the extent, change and bio-physical parameters, such as phenology and biomass. Research was conducted in different parts of the world. Knowledge and understanding gained from this book can be used for the sustainable management of mangrove forests of the worl

Machine learning algorithms for efficient process optimisation of variable geometries at the example of fabric forming

Für einen optimalen Betrieb erfordern moderne Produktionssysteme eine sorgfältige Einstellung der eingesetzten Fertigungsprozesse. Physikbasierte Simulationen können die Prozessoptimierung wirksam unterstützen, jedoch sind deren Rechenzeiten oft eine erhebliche Hürde. Eine Möglichkeit, Rechenzeit einzusparen sind surrogate-gestützte Optimierungsverfahren (SBO1). Surrogates sind recheneffiziente, datengetriebene Ersatzmodelle, die den Optimierer im Suchraum leiten. Sie verbessern in der Regel die Konvergenz, erweisen sich aber bei veränderlichen Optimierungsaufgaben, etwa häufigen Bauteilanpassungen nach Kundenwunsch, als unhandlich. Um auch solche variablen Optimierungsaufgaben effizient zu lösen, untersucht die vorliegende Arbeit, wie jüngste Fortschritte im Maschinenlernen (ML) – im Speziellen bei neuronalen Netzen – bestehende SBO-Techniken ergänzen können. Dabei werden drei Hauptaspekte betrachtet: erstens, ihr Potential als klassisches Surrogate für SBO, zweitens, ihre Eignung zur effiziente Bewertung der Herstellbarkeit neuer Bauteilentwürfe und drittens, ihre Möglichkeiten zur effizienten Prozessoptimierung für variable Bauteilgeometrien. Diese Fragestellungen sind grundsätzlich technologieübergreifend anwendbar und werden in dieser Arbeit am Beispiel der Textilumformung untersucht. Der erste Teil dieser Arbeit (Kapitel 3) diskutiert die Eignung tiefer neuronaler Netze als Surrogates für SBO. Hierzu werden verschiedene Netzarchitekturen untersucht und mehrere Möglichkeiten verglichen, sie in ein SBO-Framework einzubinden. Die Ergebnisse weisen ihre Eignung für SBO nach: Für eine feste Beispielgeometrie minimieren alle Varianten erfolgreich und schneller als ein Referenzalgorithmus (genetischer Algorithmus) die Zielfunktion. Um die Herstellbarkeit variabler Bauteilgeometrien zu bewerten, untersucht Kapitel 4 anschließend, wie Geometrieinformationen in ein Prozess-Surrogate eingebracht werden können. Hierzu werden zwei ML-Ansätze verglichen, ein merkmals- und ein rasterbasierter Ansatz. Der merkmalsbasierte Ansatz scannt ein Bauteil nach einzelnen, prozessrelevanten Geometriemerkmalen, der rasterbasierte Ansatz hingegen interpretiert die Geometrie als Ganzes. Beide Ansätze können das Prozessverhalten grundsätzlich erlernen, allerdings erweist sich der rasterbasierte Ansatz als einfacher übertragbar auf neue Geometrievarianten. Die Ergebnisse zeigen zudem, dass hauptsächlich die Vielfalt und weniger die Menge der Trainingsdaten diese Übertragbarkeit bestimmt. Abschließend verbindet Kapitel 5 die Surrogate-Techniken für flexible Geometrien mit variablen Prozessparametern, um eine effiziente Prozessoptimierung für variable Bauteile zu erreichen. Hierzu interagiert ein ML-Algorithmus in einer Simulationsumgebung mit generischen Geometriebeispielen und lernt, welche Geometrie, welche Umformparameter erfordert. Nach dem Training ist der Algorithmus in der Lage, auch für nicht-generische Bauteilgeometrien brauchbare Empfehlungen auszugeben. Weiter zeigt sich, dass die Empfehlungen mit ähnlicher Geschwindigkeit wie die klassische SBO zum tatsächlichen Prozessoptimum konvergieren, jedoch kein bauteilspezifisches A-priori-Sampling nötig ist. Einmal trainiert, ist der entwickelte Ansatz damit effizienter. Insgesamt zeigt diese Arbeit, wie ML-Techniken gegenwärtige SBOMethoden erweitern und so die Prozess- und Produktoptimierung zu frühen Entwicklungszeitpunkten effizient unterstützen können. Die Ergebnisse der Untersuchungen münden in Folgefragen zur Weiterentwicklung der Methoden, etwa die Integration physikalischer Bilanzgleichungen, um die Modellprognosen physikalisch konsistenter zu machen

Source Modulated Multiplexed Hyperspectral Imaging: Theory, Hardware and Application

The design, analysis and application of a multiplexing hyperspectral imager is presented. The hyperspectral imager consists of a broadband digital light projector that uses a digital micromirror array as the optical engine to project light patterns onto a sample object. A single point spectrometer measures light that is reflected from the sample. Multiplexing patterns encode the spectral response from the sample, where each spectrum taken is the sum of a set of spectral responses from a number of pixels. Decoding in software recovers the spectral response of each pixel. A technique, which we call complement encoding, is introduced for the removal of background light effects. Complement encoding requires the use of multiplexing matrices with positive and negative entries. The theory of multiplexing using the Hadamard matrices is developed. Results from prior art are incorporated into a singular notational system under which the different Hadamard matrices are compared with each other and with acquisition of data without multiplexing (pointwise acquisition). The link between Hadamard matrices with strongly regular graphs is extended to incorporate all three types of Hadamard matrices. The effect of the number of measurements used in compressed sensing on measurement precision is derived by inference using results concerning the eigenvalues of large random matrices. The literature shows that more measurements increases accuracy of reconstruction. In contrast we find that more measurement reduces precision, so there is a tradeoff between precision and accuracy. The effect of error in the reference on the Wilcoxon statistic is derived. Reference error reduces the estimate of the Wilcoxon, however given an estimate of theWilcoxon and the proportion of error in the reference, we show thatWilcoxon without error can be estimated. Imaging of simple objects and signal to noise ratio (SNR) experiments are used to test the hyperspectral imager. The simple objects allow us to see that the imager produces sensible spectra. The experiments involve looking at the SNR itself and the SNR boost, that is ratio of the SNR from multiplexing to the SNR from pointwise acquisition. The SNR boost varies dramatically across the spectral domain from 3 to the theoretical maximum of 16. The range of boost values is due to the relative Poisson to additive noise variance changing over the spectral domain, an effect that is due to the light bulb output and detector sensitivity not being flat over the spectral domain. It is shown that the SNR boost is least where the SNR is high and is greatest where the SNR is least, so the boost is provided where it is needed most. The varying SNR boost is interpreted as a preferential boost, that is useful when the dominant noise source is indeterminate or varying. Compressed sensing precision is compared with the accuracy in reconstruction and with the precision in Hadamard multiplexing. A tradeoff is observed between accuracy and precision as the number of measurements increases. Generally Hadamard multiplexing is found to be superior to compressed sensing, but compressed sensing is considered suitable when shortened data acquisition time is important and poorer data quality is acceptable. To further show the use of the hyperspectral imager, volumetric mapping and analysis of beef m. longissimus dorsi are performed. Hyperspectral images are taken of successive slices down the length of the muscle. Classification of the spectra according to visible content as lean or nonlean is trialled, resulting in a Wilcoxon value greater than 0.95, indicating very strong classification power. Analysis of the variation in the spectra down the length of the muscles is performed using variography. The variation in spectra of a muscle is small but increases with distance, and there is a periodic effect possibly due to water seepage from where connective tissue is removed from the meat while cutting from the carcass. The spectra are compared to parameters concerning the rate and value of meat bloom (change of colour post slicing), pH and tenderometry reading (shear force). Mixed results for prediction of blooming parameters are obtained, pH shows strong correlation (R² = 0.797) with the spectral band 598-949 nm despite the narrow range of pH readings obtained. A likewise narrow range of tenderometry readings resulted in no useful correlation with the spectra. Overall the spatial multiplexed imaging with a DMA based light modulation is successful. The theoretical analysis of multiplexing gives a general description of the system performance, particularly for multiplexing with the Hadamard matrices. Experiments show that the Hadamard multiplexing technique improves the SNR of spectra taken over pointwise imaging. Aspects of the theoretical analysis are demonstrated. Hyperspectral images are acquired and analysed that demonstrate that the spectra acquired are sensible and useful