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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. [ABSTRACT FROM AUTHOR]

To address the global phenomenon of the salinisation of large land areas, a quantitative inversion model of the salinity of saline soils and soil visible–near-infrared (NIR) spectral data was developed by considering saline soils in Zhenlai County, Jilin Province, China as the research object. The original spectral data were first subjected to Savitzky–Golay (SG) smoothing, multiplicative scattering correction (MSC) pre-processing, and a combined transformation technique. The pre-processed spectral data were then analysed to construct the difference index (DI), ratio index (RI), and normalised difference index (NDI), and the Spearman rank correlation coefficient (r) between these three spectral indices and the salt content in the samples was calculated, while a combined spectral index (r > 0.8) was eventually selected as a sensitive spectral index. Finally, a quantitative inversion model for the salinity of saline soils was developed, and the model's accuracy was evaluated based on partial least squares regression (PLSR), the random forest (RF) algorithm, and the radial basis function (RBF) neural network algorithm. The results indicated that the inversion of soil salt content using the selected combination of spectral indices based on the RBF neural network algorithm was the most effective, with the prediction model yielding an R2 value of 0.950, a root mean square error (RMSE) of 1.014, and a relative percentage deviation (RPD) of 4.479, which suggested a good prediction effect. [ABSTRACT FROM AUTHOR]

The mining waste of open pit mines is usually piled-up in dump sites, making a man-made hill more than tens of meters high. Because of the loose structure of the dump sites, landslides or debris flow may occur after heavy rainfall, threatening local lives and properties. Therefore, dump stability analysis is crucial for ensuring local safety. In this paper, a collaborative stability analysis based on multiple remote sensing technologies was innovatively conducted at the Xudonggou dump of the Anqian iron mine. A small baseline subset (SBAS) analysis was used to derive the spatial and temporal distributions of displacements in the line-of sight (LOS) over the whole study area. The deformation in LOS is translated to the slope direction based on an assumption that displacements only occur parallel to the slope surface. Infrared Thermography (IRT) technology was used to detect weak aquifer layers located at the toe of possible landslide bodies. Then, numerical simulations based on the limit equilibrium method were conducted to calculate the factor of safety for three profiles located on the dump site. The results, emerging from multiple remote sensing technologies, were very consistent and, eventually, the landslide hazard zone of the Xudonggou dump site was outlined. [ABSTRACT FROM AUTHOR]