Research and Verification of a Remote Sensing BIF Model Based on Spectral Reflectance Characteristics

Abstract

Banded iron formations (BIFs) represent the most important and widely distributed iron ore resources in the world. Monitoring BIF resources is of great significance for tracking reserves and for the orderly exploitation of resources. Previous remote sensing (RS) recognition methods for ground objects in mining areas have been developed based on satellite sensor data, but those methods do not incorporate the spectral characteristics of ground objects. Consequently, these modeling methods are relatively blind because they do not consider the real spectra of ground objects in advance, affecting the accuracy of RS recognition to a certain degree. In the present study, the visible to near-infrared spectra of two types of typical iron ore (magnetite and hematite) and of the surrounding rocks in a BIF deposit were first recorded by a field-portable spectrometer in a mining area. Then, the differences in the spectral characteristics among the two types of iron ore and surrounding rocks were analyzed. Based on the different spectral characteristics in different wavelength ranges, RS extraction and classification models for iron ore were constructed and applied to Landsat 8 data for the actual recognition of iron ore in an open pit. This study yielded the following results. There were remarkable differences in the spectral characteristics among the two types of iron ore and surrounding rocks. In addition to distinguishing the iron ore from the surrounding rocks, magnetite and hematite were further differentiated based on the constructed inversion model. The accuracies of distinguishing iron ore from the surrounding rocks, identifying hematite, and identifying magnetite were 83.5%, 83.3%, and 85%, respectively. The results demonstrated that iron ore zones can be identified automatically by the RS model based on the measured spectral characteristics and the inversion model. Accordingly, this model can increase the identification accuracy and provide a new method for BIF deposit detection and exploitation monitoring.