Abstract
Detecting surface mineral indicators with high spatial precision remains a significant challenge in mineral exploration, particularly in remote or geologically complex regions such as Eastern Kazakhstan. This study addresses this challenge by integrating high-resolution multispectral imagery from Unmanned Aerial Vehicles (UAVs) to map iron oxide distributions, key indicators of ore mineralization such as copper porphyry at the Kyzylkiya mining site in Eastern Kazakhstan. The novelty of this study is the development of a statistical fuzzy logic model that integrates UAV-derived spectral indices, including the Normalized Difference Vegetation Index (NDVI) and targeted band ratios, to generate probabilistic maps of iron oxide presence at a fine spatial resolution of 5.29 cm. This approach enhances prediction accuracy by incorporating uncertainty and variability in spectral responses. The model’s output was validated through a multi-stage process involving independent multispectral datasets and ground-truth sampling, achieving an overall accuracy of 80%. The results reveal concentrated iron oxide anomalies in the northeast and northwest of the study area, underscoring the method’s effectiveness. This integrated UAV-fuzzy logic framework demonstrates a scalable and cost-effective solution for early-stage mineral exploration and can be adapted to similar geological settings globally.