engleski

Geological mapping in the karst terrains in Dalmatia using digital satellite images as an integral part in mineral raw materials exploration

The possibility of karst terrain lithological mapping using remote sensing methods is considered in three pilot areas. The aim is to determine if such methods can enhance existing geological maps which are used in mineral exploration studies. The first research area with a size of approximately 100 square kilometers is located in Šibenik - Knin County. This area consists of Senonian limestones, Eocene limestones, marls and conglomerates, and Quaternary sediments. These sediments have potential for exploitation as ornamental stone and/or crushed stone aggregates. The second research area is the wider area of the town of Vrlika in Split-Dalmatia County. The study of the potentiality of mineral raw materials in the wider area of Vrlika (Lukšić and Gabrić, 1997) showed a high geological potential for gypsum exploitation. The third pilot area is the island of Brač since mineral raw materials important for the Republic of Croatia are exploited there. In this part of the Republic of Croatia, rudist limestones are exploited as ornamental stone and/or crushed-stone aggregates. Simultaneously with the preprocessing and processing of legacy data, available satellite imagery (e.g. Sentinel-2A, WorldView 3, Pleiades, and Planet Sope). and spectral indices of the USGS spectral library will be compiled. High-resolution DEM creation using archival stereo pairs, UAV or ICEYE SAR data will also be a part of the entire data processing workflow within a GIS environment. In addition, machine learning algorithms and discriminant function analysis will also be utilized. The purpose of the described multidisciplinary research is to study geological properties using a variety of different approaches with as many features as possible to increase the accuracy of lithological unit discrimination and mineral resources mapping. This novel methodological approach will generate information on the geological potentiality related to mineral raw materials and save time that is necessary to re-map these areas.

geological mapping ; mineral raw materials ; imagery processing ; machine learning ; discriminant function analysis

'Project supported by ESA Network of Resources Initiative'