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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Emissividade dos atributos do solo via sensores terrestres e de satélite / Emissivity of soil attributes via terrestrial and satellite sensors

Urbina Salazar, Diego Fernando 05 February 2019 (has links)
A textura e o conteúdo do carbono orgânico (CO) influenciam na resposta espectral dos solos. O estudo desses atributos é de grande importância para a preservação e o manejo adequado da terra na busca de uma agricultura sustentável. O uso de sensores de laboratório e satélites tem se mostrado como uma ferramenta no auxílio para o estudo destes, porém a análise dos atributos do solo com esses sensores tem focado principalmente nas regiões do espectro eletromagnético do visível (Vis), infravermelho próximo (NIR) e infravermelho de ondas curtas (SWIR), com poucos estudos no infravermelho médio (MIR). O objetivo deste trabalho foi identificar o padrão espectral do solo com diferentes granulometrias (areia e argila) e teores de CO utilizando sensores de laboratório e satélite na região do MIR, especificamente na faixa do infravermelho termal (TIR). O estudo teve uma avaliação qualitativa e quantitativa da argila, CO e das frações de areia (fina e grossa). A área de estudo está localizada na região de Piracicaba, São Paulo, Brasil. Foram coletadas 150 amostras de solo a uma profundidade de 0-20 cm. A textura do solo foi determinada pelo método da pipeta e a porcentagem de CO via combustão seca. Dados espectrais em refletância e emissividade (ε) foram adquiridos com o sensor Fourier Transform Infrared (FT-IR) Alpha (Bruker optics Corporation). Uma imagem \"ASTER_05\" foi adquirida em 15 de julho de 2017 em valores de ε. As amostras foram separadas por classes texturais e o comportamento espectral no TIR foi descrito. Os dados obtidos pelo sensor de laboratório foram reamostrados para as bandas do sensor de satélite. O comportamento entre os espectros de ambos sensores foi semelhante e teve correlação significativa com os atributos estudados, principalmente para areia. Para os modelos de regressão por mínimos quadrados parciais (PLSR), foram utilizadas seis estratégias (MIR, MIR_ASTER, ASTER, Termal, Termal IDC e MIR IDC) que consistiram no uso de todas as bandas de sensores, ou pela seleção das mesmas que apresentaram as correlações mais significativas com cada um dos atributos. Os modelos apresentaram um bom desempenho na predição de todos os atributos usando o MIR inteiro. No TIR, o modelo para areia total e para as frações fina e grossa foi bom. No caso dos modelos criados com os dados do sensor ASTER, não foram tão promissores quanto os de laboratório. O uso de bandas específicas ajudou a estimar alguns atributos no MIR e no TIR, aumentando o desempenho preditivo melhorando a validação dos modelos. Portanto, a discriminação dos atributos do solo com sensores de satélite pode ser melhorada com a identificação de bandas específicas, como observado nos resultados com sensores de laboratório. / Soil texture and organic carbon (OC) content influence its spectral response. The study of these attributes is relevant for the preservation and proper management of land in pursuit of a sustainable agriculture. Laboratory and satellite sensors have been applied as a useful tool for studying soil attributes, but their analysis with these sensors has mainly focused on the visible (Vis), near infrared (NIR) and shortwave infrared (SWIR) regions of the electromagnetic spectrum, with few studies in the Medium Infrared (MIR). The objective of this study was to identify the spectral pattern of soils with different granulometry (sand and clay) and OC content using laboratory and satellite sensors in the MIR region, specifically in the Thermal Infrared (TIR) range. This study had qualitative and quantitative analyses of clay, OC and sand fractions (fine and coarse). The study area is located in the region of Piracicaba, São Paulo, Brazil. 150 soil samples were collected at a depth of 0-20 cm. Soil texture was determined by the pipette method and the percentage of OC via dry combustion. Reflectance and emissivity (ε) spectral data were obtained with the Fourier Transform Infrared (FT-IR) Alpha sensor (Bruker Optics Corporation). An image \"ASTER_05\" from July 15, 2017 was acquired with values of ε. Samples were separated by textural classes and the spectral behavior in the TIR region was described. The data obtained by the laboratory sensor were resampled to the satellite sensor bands. The behavior between spectra of both sensors was similar and had significant correlation with the studied attributes, mainly sand. For the partial least squares regression (PLSR) models, six strategies were used (MIR, MIR_ASTER, ASTER, Thermal, Thermal IDC and MIR IDC), which consisted in the use of all sensors bands, or by the selection of bands that presented the most significant correlations with each one of the attributes. Models presented a good performance in the prediction of all attributes using the whole MIR. In the TIR, models for total sand content and for fine and coarse fractions were good. In the case of models created with ASTER sensor data, they were not as promising as those with laboratory data. The use of specific bands was useful in estimating some attributes in the MIR and TIR, improving the predictive performance and validation of models. Therefore, the discrimination of soil attributes with satellite sensors can be improved with the identification of specific bands, as observed in the results with laboratory sensors.
2

Étude multi-échelle de la température de surface des cours d’eau par imagerie infrarouge thermique : exemples dans le bassin du Rhône / Multi-scale study of river surface temperature using thermal infrared remote sensing : examples in the Rhône basin (South East France)

Wawrzyniak, Vincent 12 December 2012 (has links)
Dans un contexte de changement climatique, la compréhension du régime thermique des cours d’eau est un enjeu important. En mesurant le rayonnement dans le spectre électromagnétique de l’infrarouge thermique (IRT : 0,4-14µm), la télédétection IRT offre la possibilité d’obtenir une cartographie de la température de surface à différentes échelles spatiales. L’approche multi-échelle est ainsi le fil directeur de ce travail.Dans le premier temps, nous utilisons des images satellites Landsat ETM+ pour caractériser les structures thermiques longitudinales et temporelles d’un grand continuum fluvial : le Rhône français (500 km). Une méthode automatique supprimant les pixels contaminés par les entités exondées, est développée, améliorant ainsi la précision des données. Les images nous permettent de comprendre les effets thermiques des affluents et des centrales nucléaires. L’Isère est la principale source d’eau froide, alors que les centrales nucléaires du Bugey, de Saint-Alban et de Tricastin réchauffent le fleuve. Nous mettons en évidence des anomalies thermiques au niveau des aménagements hydroélectriques. Par rapport aux canaux, les Rhône court-circuités (RCC) sont plus sensibles aux conditions extérieures du fait de leur géométrie et de leurs conditions hydrauliques.Dans un second temps, les travaux se focalisent sur un tronçon plus court (50 km) : l’Ain dans sa basse vallée où quatre campagnes IRT aéroportées sont réalisées. Nous développons une méthode statistique permettant de calculer l’incertitude de mesure associée à la construction des profils longitudinaux de température de l’eau. Les artefacts des vraies tendances longitudinales sont ainsi différenciés. Pour comprendre ces tendances, un modèle 1D (thermo-hydraulique) est mis en place sur 21 kilomètres. Il considère les flux de chaleur à l’interface eau-air et les propriétés géométriques ainsi qu’hydrauliques de la rivière. Les arrivées phréatiques associées aux bras morts et aux suintements latéraux sont identifiées sur les images thermiques et intégrées au modèle. Ces arrivées phréatiques peuvent refroidir l’Ain de 0,6°C en été lorsqu’elles représentent 15,7% du débit total.Une échelle plus fine est explorée enfin. Le travail porte cette fois sur neuf tronçons en tresses (1 km) pour lesquels des images IRT à très haute résolution spatiale sont acquises. En caractérisant les distributions spatiales de la température, nous identifions deux types de tronçons. Le premier montre une très faible variabilité thermique spatiale tout au long de la journée. Les cours d’eau de ce type ont bien souvent un régime hydrologique proglaciaire avec des débits estivaux élevés, ce qui tend à homogénéiser la température. Le second type présente une hétérogénéité thermique élevée. La température des chenaux courants varie avec la température de l'air. En revanche, la température des chenaux alimentés par des eaux souterraines est relativement constante au cours de la journée. Nous proposons une méthode ne nécessitant pas d’images IRT pour identifier les tronçons montrant une variabilité thermique élevée.À travers ce travail, nous montrons qu’il est nécessaire de coupler les approches spatiales et temporelles pour comprendre la température des cours d’eau. Longtemps, les mesures ont été effectuées avec des thermomètres. L’aspect spatial a ainsi souvent été ignoré. La télédétection IRT a permit de mieux appréhender les structures spatiales de température. Toutefois, pour comprendre ces dernières il est indispensable de considérer les changements temporels de température. Il est également nécessaire d’intégrer une approche plus physique permettant de simuler différentes situations pour évaluer l’importance des différents facteurs affectant la température. / In a context of global warming, understanding the thermal regime of rivers is a key issue. By measuring the radiation in the electromagnetic spectrum of thermal infrared (TIR: 0.4-14µm), TIR remote sensing offers the possibility of obtaining surface temperature maps at multiple scales. The multi-scale approach is thus the guiding principle of this work.First we use satellite thermal infrared images from Landsat ETM+ to investigate longitudinal and temporal variations in the thermal patterns of a large river continuum, the French Rhône (500 km). An automated water extraction technique is developed to remove pixels contaminated by terrestrial surfaces. This method improves the accuracy of our data. The images allow us to understand the thermal effects of tributaries and nuclear power plants: the Isère is the main source of cold water while the Bugey, Saint-Alban and Tricastin nuclear power plants warm the river. We show temperature differences within the largest hydroelectric bypass facilities between the bypass section and the canal. The factors responsible for these differences are the length and minimum flow of the bypass section as well as tributaries coming into this reach.Second, we focus on a shorter river (50 km): the lower Ain in France where four airborne TIR surveys are performed. Based on a statistical analysis of temperature differences between overlapping images we calculate the measurement uncertainty associated with TIR derived profiles. This uncertainty allows for the discrimination between artifacts and real longitudinal thermal trends. To understand these trends, we use a 1D determinist model which predicts water temperature at an hourly time step along a 21 km reach. The model considers heat fluxes at the water-air interface as well as the geometrical and hydraulic characteristics of the river. Based on TIR images, groundwater inputs associated with backwaters and lateral seepages are identified. They are inserted into the temperature model. These groundwater inputs can mitigate high water temperatures during the summer by cooling the river up to -0.6°C when they represent 15.7% of the total discharge.A finer scale is finally explored. The work focuses on nine braided reaches located in the French Alps (1 km) where very high spatial resolution TIR images are acquired. By characterizing the spatial distributions of water temperature, we identify two types of reaches. The first type shows a very low thermal spatial variability throughout the day. Rivers of this type often have a proglacial hydrological regime with high summer flows, which tends to homogenize the temperature. The second type exhibits a higher thermal variability with changes during the day. The temperature of flowing channels changes during the daytime according to the air temperature. In contrast, the temperature of groundwater-fed channels exhibits smaller changes which creates thermal variability over space and time. We propose a method which does not require TIR images in order to identify reaches showing high thermal variability.Through this work, we show that it is essential to combine both spatial and temporal approaches to understand river temperature. Thermometers have been used for many years. Thus, the spatial aspect has often been ignored. TIR remote sensing has allowed a better characterization of spatial thermal patterns. However, to understand these patters it is necessary to consider temporal changes of water temperature. It is also necessary to integrate a more physical approach in order to simulate different scenarios and to assess the importance of the different factors affecting water temperature.

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