Mr. Ubaldo Gemusse Profile

Ubaldo Gemusse

at Univ Licungo

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Proceedings Article | 3 October 2019 Paper

Comparing different techniques of satellite imagery classification to mineral mapping pegmatite of Muiane and Naipa: Mozambique

Ubaldo Gemusse, Alexandre Lima, Ana Teodoro

Proceedings Volume 11156, 111561E (2019) https://doi.org/10.1117/12.2532570

KEYWORDS: Image classification, Satellites, Earth observing sensors, Landsat, Geology

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Several scientific studies with different concept on the mapping of pegmatites have been done in Muiane and Naipa (Mozambique) region. However, none of the studies compare different satellite data and different remote sensing classification algorithms. This study aims to compare the land cover/use classification maps and their accuracies considered sentinel-2, aster, and Landsat OLI imagery. The algorithms employed to evaluate the pegmatites location at Naipa and muiane in alto ligonha pegmatite district were minimum distance (MinD), spectral angle mapper (SAM), and maximum likelihood (ML). The identified features of landscape characteristics selected includes 8 class (kaolinite; montmorillonite; water; built up; bare soil; grasslands; shrubs; isolated bush). The results showed that SAM and MinD algorithms are appropriate for mineralogical mapping validated with ground truth data and geological maps. A kappa index of 0.85 and an overall accuracy (OA) of 80% was obtained for SAM algorithm, and a kappa of 0,80 and OA of 90% for the MinD algorithm. The classification of the images using SAM and mind showed better results for the clays (kaolinite, montmorillonite) visible in both classifications, has also been tested unsupervised classifications or criteria determined by the geologist using an input training dataset in the case of supervised classifications.

Proceedings Article | 9 October 2018 Paper

Pegmatite spectral behavior considering ASTER and Landsat 8 OLI data in Naipa and Muiane mines (Alto Ligonha, Mozambique)

Ubaldo Gemusse, Alexandre Lima, Ana Teodoro

Proceedings Volume 10790, 107901L (2018) https://doi.org/10.1117/12.2325555

KEYWORDS: Earth observing sensors, Landsat, Minerals, Mining, Short wave infrared radiation, Lithium, Sensors, Image processing, Associative arrays, Remote sensing

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The Naipa and Muiane mines are located on the Nampula complex, a stratigraphic tectonic subdivision of the Mozambique Belt, in the Alto Ligonha region. The pegmatites are of the Li-Cs-Ta type, intrude a chlorite phyllite and gneisses with amphibole and biotite. The mines are still active. The main objective of this work was to analyze the pegmatite’s spectral behavior considering ASTER and Landsat 8 OLI data. An ASTER image from 27/05/2005, and an image Landsat OLI image from 02/02/2018 were considered. The data were radiometric calibrated and after atmospheric corrected considered the Dark Object Subtraction algorithm available in the Semi-Automatic Classification Plugin accessible in QGIS software. In the field, samples were collected from lepidolite waste pile in Naipa and Muaine mines. A spectroadiometer was used in order to analyze the spectral behavior of several pegmatite’s samples collected in the field in Alto Ligonha (Naipa and Muiane mines). In addition, QGIS software was also used for the spectral mapping of the hypothetical hydrothermal alterations associated with occurrences of basic metals, beryl gemstones, tourmalines, columbite-tantalites, and lithium minerals. A supervised classification algorithm was employed - Spectral Angle Mapper for the data processing, and the overall accuracy achieved was 80%. The integration of ASTER and Landsat 8 OLI data have proved very useful for pegmatite’s mapping. From the results obtained, we can conclude that: (i) the combination of ASTER and Landsat 8 OLI data allows us to obtain more information about mineral composition than just one sensor, i.e., these two sensors are complementary; (ii) the alteration spots identified in the mines area are composed of clay minerals. In the future, more data and others image processing algorithms can be applied in order to identify the different Lithium minerals, as spodumene, petalite, amblygonite and lepidolite.

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