Evaluation of remote sensing data potential in the geological exploration of Freixeda area (Mirandela, Portugal). A preliminary study

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2014

Autores

Alexandre Lima (Autor) FCUP; Teodoro, AC (Autor) FCUP; Casimiro, JP (Autor) FCUP

Ata de Conferência Internacional

Título: EARTH RESOURCES AND ENVIRONMENTAL REMOTE SENSING/GIS APPLICATIONS V

Conference on Earth Resources and Environmental Remote Sensing/GIS Applications V

Amsterdam, NETHERLANDS, SEP 23-25, 2014

Indexação

ISI Proceedings

ISI Web of Knowledge - 0 Citações

Scopus - 1 Citação

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da terra e ciências do ambiente

CORDIS: Ciências Tecnológicas > Tecnologia

Outras Informações

ID Authenticus: P-00A-5SB

DOI: 10.1117/12.2067183

Abstract (EN): The value of remote sensing data to geological exploration has increased as technology has improved. The advent of multispectral and hyperspectral imaging has allowed surface mapping to be performed remotely, thereby enabling vast areas to be mapped in a short time at a fraction of the cost of traditional geologic mapping. Different scanning spectrums enabled researchers to begin cataloguing various reflection and adsorption properties of soils, rock, and vegetation. These spectra could be used to interpret actual surface lithologies from remote sensing images. The study area focused in this work was the Freixeda stretch, district of Mirandela, Portugal. In this work, an ASTER image (March 2011) from the study area was used. ASTER VNIR and SWIR reflectance data have been used to produce colour composite images that seek to maximize the lithological information in the area; ratio images have been used to highlight ferric iron; and relative band depth images of the SWIR bands have been used to predict the occurrence of Alunite/Pyrophyllite, Kaolinite, Illite and Prophylitic group minerals. The VNIR bands were used to define vegetation and also ferric iron (defined by the ratio of band 2/band 1). The vegetation ratio is defined by the ratio of band 3/band 2. The SWIR data consists of 6 bands. Band 4 is located where most cover types have maximum reflectivity. Bands 5-9 cover an area of the SWIR where many-OH bearing minerals and carbonate minerals have absorption features. The presence of Au and Ag mineralization confirm the richness of this area.

Idioma: Inglês

Tipo (Avaliação Docente): Científica