Satelites estabilizados por rotação e torque magnético residual

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Variáveis canônicas não singulares e o movimento rotacional de satélites artificiais

Pós-graduação em Física - FEG

Estudo numérico da captura gravitacional temporária utilizando o problema de quatro corpos

Pós-graduação em Física - FEG

Cálculo de coeficientes de arrasto para satélites artificiais

Pós-graduação em Física - FEG

Pertubação orbital devida a um terceiro corpo com distribuição não uniforme de massa e em órbita elíptica

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Estabilidade de órbitas congeladas em torno de satélites planetários utilizando o sistema hamiltoniano na forma normal

Pós-graduação em Física - FEG

Análise da estabilidade do movimento rotacional de satélites artificiais com quatérnions e sob a influência de torques externos

The aim of this work is to analyze the stability of the rotational motion’s artificial satellite using the Routh Hurwitz Algorithm (CRH) and the quaternions to describe the satellite’s attitude. This algorithm allows the investigation of the stability of the motion using the coefficients of the characteristic equation associated with the equation of the rotational motion in the linear form. The equations of the rotational motion are given by the four cinematic equations for the quaternion and the three equations of Euler for the spin velocity’s components. In the Euler equations are included the components of the gravity gradient torque (TGG) and the solar radiation torque (TRS). The TGG is generated by the difference of the Earth gravity force direction and intensity actuating on each satellite mass element and it depends on the mass distribution and the form of the satellite. The TRS is created by changing of the linear momentum, which happens due to the interactions of solar photons with the satellite surface. The equilibrium points are gotten by the equation of rotational motion and the CRH is applied in the linear form of these equations. Simulations are developed for small and medium satellites, but the gotten equilibrium points are not stable by CRH. However, when some of the eigenvalues of the characteristic equation are analyzed, it is found some equilibrium points which can be pointed out as stables for an interval of the time, due to small magnitude of the real part of these eigenvalue

Hierarchical artificial immune system for crop stage classification

This paper presents a new hierarchical clustering algorithm for crop stage classification using hyperspectral satellite image. Amongst the multiple benefits and uses of remote sensing, one of the important application is to solve the problem of crop stage classification. Modern commercial imaging satellites, owing to their large volume of satellite imagery, offer greater opportunities for automated image analysis. Hence, we propose a unsupervised algorithm namely Hierarchical Artificial Immune System (HAIS) of two steps: splitting the cluster centers and merging them. The high dimensionality of the data has been reduced with the help of Principal Component Analysis (PCA). The classification results have been compared with K-means and Artificial Immune System algorithms. From the results obtained, we conclude that the proposed hierarchical clustering algorithm is accurate.

Multi-Temporal Satellite Image Analysis Using Gene Expression Programming

This paper discusses an approach for river mapping and flood evaluation to aid multi-temporal time series analysis of satellite images utilizing pixel spectral information for image classification and region-based segmentation to extract water covered region. Analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images is applied in two stages: before flood and during flood. For these images the extraction of water region utilizes spectral information for image classification and spatial information for image segmentation. Multi-temporal MODIS images from ``normal'' (non-flood) and flood time-periods are processed in two steps. In the first step, image classifiers such as artificial neural networks and gene expression programming to separate the image pixels into water and non-water groups based on their spectral features. The classified image is then segmented using spatial features of the water pixels to remove the misclassified water region. From the results obtained, we evaluate the performance of the method and conclude that the use of image classification and region-based segmentation is an accurate and reliable for the extraction of water-covered region.

Oceanographic, meteorological, satellite and aircraft observations for project Little Window 2: May 1971

ENGLISH: In May 1971, a joint united states - Mexican experiment, Project Little Window 2, (LW-2) involving data collected by satellite, aircraft and ship sensors was made in the southern part of the Gulf of California. LW-2 was planned as an improved and enlarged version of LW-l (conducted the previous year; Stevenson and Miller, 1971) with field work scheduled to be made within a 200 by 200 km square region in the Gulf of California. The purposes of the new field study were to determine through coordinated measurements from ships, aircraft and satellites, the utility of weather satellites to measure surface temperature features of the ocean from space and specifically to evaluate the high resolution infrared sensors aboard N~ 1, ITOS 1 and NIMBUS 4 and to estimate the magnitude of the atmospheric correction factors needed to bring the data from the spacecraft sensors into agreement with surface measurements. Due to technical problems during LW-2, however, useful data could not be obtained from ITOS 1 and NIMBUS 4 so satellite information from only NOAA-1 was available for comparison. In addition, a new purpose was added, i.e., to determine the feasibility of using an Automatic picture Transmission (APT) receiver on shore and at sea to obtain good quality infrared data for the local region. SPANISH: En mayo 1971, los Estados Unidos y México realizaron un experimento en conjunto, Proyecto Little Window 2 (LW-2), en el que se incluyen datos obtenidos mediante captadores de satélites, aviones y barcos en la parte meridional del Golfo de California. Se planeó LW-2 para mejorar y ampliar el proyecto de LW-l (conducido el año anterior; Stevenson y Miller, 1971), realizándose el trabajo experimental en una región de 200 por 200 km cuadrados, en el Golfo de California. El objeto de este nuevo estudio experimental fue determinar mediante reconocimientos coordinados de barcos, aviones y satélites la conveniencia de los satélites meteorológicos para averiguar las características de la temperatura superficial del océano desde el espacio, y especialmente, evaluar los captadores infrarrojos de alta resolución a bordo de NOAA 1, ITOS 1 Y NIMBUS 4, y estimar la magnitud de los factores de corrección atmosféricos necesarios para corregir los datos de los captadores espaciales para que concuerden con los registros de la superficie. Sin embargo, debido a problemas técnicos durante LW-2, no fue posible obtener datos adecuados de ITOS 1 y NIMBUS 4, as1 que solo se pudo disponer de la información de NOAA 1 para hacer las comparaciones. Además se quiso determinar la posibilidad de usar un receptor de Trasmisión Automático de Fotografias (APT) en el mar para obtener datos infarojos de buena calidad en la región local. (PDF contains 525 pages.)

Macroscale factors affecting diatom abundance: a synergistic use of Continuous Plankton Recorder and satellite remote sensing data

Diatoms exist in almost every aquatic regime; they are responsible for 20% of global carbon fixation and 25% of global primary production, and are regarded as a key food for copepods, which are subsequently consumed by larger predators such as fish and marine mammals. A decreasing abundance and a vulnerability to climatic change in the North Atlantic Ocean have been reported in the literature. In the present work, a data matrix composed of concurrent satellite remote sensing and Continuous Plankton Recorder (CPR) in situ measurements was collated for the same spatial and temporal coverage in the Northeast Atlantic. Artificial neural networks (ANNs) were applied to recognize and learn the complex non-monotonic and non-linear relationships between diatom abundance and spatiotemporal environmental factors. Because of their ability to mimic non-linear systems, ANNs proved far more effective in modelling the diatom distribution in the marine ecosystem. The results of this study reveal that diatoms have a regular seasonal cycle, with their abundance most strongly influenced by sea surface temperature (SST) and light intensity. The models indicate that extreme positive SSTs decrease diatom abundances regardless of other climatic conditions. These results provide information on the ecology of diatoms that may advance our understanding of the potential response of diatoms to climatic change.

Predicting forest attributes In southeast Alaska using artificial neural networks

Artificial neural network (ANN) methods are used to predict forest characteristics. The data source is the Southeast Alaska (SEAK) Grid Inventory, a ground survey compiled by the USDA Forest Service at several thousand sites. The main objective of this article is to predict characteristics at unsurveyed locations between grid sites. A secondary objective is to evaluate the relative performance of different ANNs. Data from the grid sites are used to train six ANNs: multilayer perceptron, fuzzy ARTMAP, probabilistic, generalized regression, radial basis function, and learning vector quantization. A classification and regression tree method is used for comparison. Topographic variables are used to construct models: latitude and longitude coordinates, elevation, slope, and aspect. The models classify three forest characteristics: crown closure, species land cover, and tree size/structure. Models are constructed using n-fold cross-validation. Predictive accuracy is calculated using a method that accounts for the influence of misclassification as well as measuring correct classifications. The probabilistic and generalized regression networks are found to be the most accurate. The predictions of the ANN models are compared with a classification of the Tongass national forest in southeast Alaska based on the interpretation of satellite imagery and are found to be of similar accuracy.

Validation of improved TAMANN neural network for operational satellite-derived rainfall estimation in Africa

Real-time rainfall monitoring in Africa is of great practical importance for operational applications in hydrology and agriculture. Satellite data have been used in this context for many years because of the lack of surface observations. This paper describes an improved artificial neural network algorithm for operational applications. The algorithm combines numerical weather model information with the satellite data. Using this algorithm, daily rainfall estimates were derived for 4 yr of the Ethiopian and Zambian main rainy seasons and were compared with two other algorithms-a multiple linear regression making use of the same information as that of the neural network and a satellite-only method. All algorithms were validated against rain gauge data. Overall, the neural network performs best, but the extent to which it does so depends on the calibration/validation protocol. The advantages of the neural network are most evident when calibration data are numerous and close in space and time to the validation data. This result emphasizes the importance of a real-time calibration system.