Analysis of Landsat 8 satellite images by principal components and MNF for the detection of areas of the Repsol oil spill on the Peruvian coast
DOI:
https://doi.org/10.53673/th.v2i2.110Keywords:
Landsat 8, derrame de petróleo, Repsol, normalización, procesamiento de imágenes, ACP y MNFAbstract
The pollution from the oil spill on the Peruvian coast caused by the Italian-flagged ship Mare Doricum, owned by the Spanish company Repsol, not only seriously affected marine flora and fauna but also hundreds of families of Peruvian fishermen, leaving them without work. For this reason, monitoring the effects of these incidents is very important for public health and environmental protection. Satellite missions are a very efficient tool to identify contaminants such as oil spills. The purpose of this study is to detect the area of the oil spill using the Landsat 8 multispectral sensor. To detect the oil spill with greater precision, various preprocessing was performed on the Landsat 8 satellite image, such as conversion to reflectance, normalization with the Bandmax- min of the image of the affected area and finally the methods of principal component analysis (ACP) and the Minimum Noise Fraction (MNF) were performed. All the results were analyzed by programming the free software R, these experimental results clearly indicated that the MNF method is the most suitable to detect the oil spill with greater precision through Landsat 8 multispectral images.
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