GIS-based machine learning algorithms for mapping beach placer deposits in the southwest coast of India using Landsat-8 OLI images

Abstract

Beach placer deposits are economically enriched with Fe-Ti oxide, Zr, and rare earth minerals such as garnet, rutile, zircon, monazite, ilmenite, and sillimanite. Landsat 8-OLI image provides the distinct spectral signature of placer deposits in its multispectral bands that can be used as a key source for mapping placer mineral deposits. GIS-based machine learning algorithms (MLAs) are used for executing the subpixel-based classification of high-dimensional data products (multispectral images, i.e., Landsat 8-OLI image) based on its spectral signatures for mapping the placer mineral deposits. The Landsat 8-OLI images are executed using the four widely used MLAs such as maximum likelihood classification, random forest classifier (RFC), support vector machine, and artificial neural network for mapping placer mineral deposits in the southwest coastal stretches of Thiruvananthapuram district in the Kerala state of India. The resultant map shows the occurrence of placer deposits with a major concentration of ilmenite. The accuracy assessment reveals that RFC shows better performance for mapping beach minerals with an overall accuracy of 84.4925% and kappa coefficient of 0.8184. Our study proves that the MLAs are the vital tools for mapping the placer mineral deposits in the sandy beaches, and thereby, this approach is being used as a scientific key tool for opening the gateways for beach mineral mapping using multispectral satellite images.