Interferência da precipitação em variáveis ambientais utilizadas para detecção de mudanças no município de Petrolina - PE

• Autor(a) principal: SILVA, Douglas Alberto de Oliveira
• Data de Publicação: 2017
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UFRPE
• Texto Completo: http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/7645
• topic: Precipitação (Meteorologia); Degradação ambiental; Sensoriamento remoto; CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA

In recent years, with the advancement of remote sensing based on satellite images, it has become possible to monitor large areas of the various biomes in the world. In this context, it is possible to analyze and interpret possible changes in the surface of the Earth caused by processes that are naturals or even anthropics. The objective of this study was to analyze the influence of accumulated precipitation on the environmental variables used to detect changes in occupancy and land use in the city of Petrolina-PE, using surface data and images of the TRMM and Landsat-8 satellites for the years from 2013 to 2015. Remote sensing techniques were used to estimate vegetation indices, albedo, surface temperature and accumulated precipitation of 02, 08, 16, 30, 45, 60, 75, 105, 115, 120 days before the passage of the Landsat-8 satellite. Then, the accumulated precipitation of the automatic weather stations were correlated with the biophysical parameters and the accumulated precipitation of the TRMM satellite. The accumulated precipitation was modeled using multiple linear regression. The change detection techniques and decision tree classification were applied to infer about occupancy and land use. The results showed that the vegetation indexes and the techniques of monitoring the degradation and detection of changes, satisfactorily identified the behavior of the surrounding vegetation in the city of Petrolina - PE, standing out as indicators of areas in the process of degradation in the semiarid. It is concluded that cumulative precipitation in 2 and 45 days before the passage of the Landsat-8 satellite correlated very well with biophysical parameters and TRMM data. Precipitation interfered more with albedo and surface temperature. In addition, the change detection techniques and decision tree classification were efficients in identifying anthropic areas during the three years studied.