Detalhes bibliográficos
| Ano de defesa: |
2010 |
| Autor(a) principal: |
SOARES, Marcos Flaviano Matos
 |
| Orientador(a): |
STOSIC, Borko |
| Banca de defesa: |
OLIVEIRA JÚNIOR, Wilson Rosa de,
REN, Tsang Ing |
| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Universidade Federal Rural de Pernambuco
|
| Programa de Pós-Graduação: |
Programa de Pós-Graduação em Biometria e Estatística Aplicada
|
| Departamento: |
Departamento de Estatística e Informática
|
| País: |
Brasil
|
| Palavras-chave em Português: |
|
| Palavras-chave em Inglês: |
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| Área do conhecimento CNPq: |
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| Link de acesso: |
http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/5212
|
Resumo: |
The use of modern computational resources together with the development of mathematical, statistical and computational techniques in the treatment of geographical information, has been important a better understanding of patterns of geographically distributed data. An application of these resources is the detection and interpretation of the spatial distribution of hotspots, a term used for elements registered by satellite sensors of surface regions with high temperature. The number of hot pixels should not be identified with the number of fires, because there are factors that can influence real fire detection, where both omission and false identification are possible. The present work aims at identification of space correlations of hotspots in Brazil detected by the satellite NOAA 12, during the period 1998- 2006, and provide aid in the choice of theoretical models inferential express themselves through spatial distribution across the spatial correlation property of stochastic processes generating this phenomenon, using the method to calculate the fractal dimension developed by Grassberger and Proccacia. It is found that spatial distribution of hot pixels for individual years under study demonstrates fractal behavior with the correlation dimension approximately DCORR 1.6. The value of the fractal dimension for data grouped by month along the whole period, is also close to 1.6, except for the months of january and april which display two regions of fractal behavior, with DCORR 1.0 for distances below 10km, and DCORR 1.6 for larger distance. This behavior suggests possible multifractality, and requires further phenomenological studies to be well understood. The results of the current work should be taken into account in the development and validation of theoretical and computational models of the stochastic processes of this phenomenon, as well as related phenomena, such as e.g. carbon emission. |
