We are the first to apply the copper stress vegetation index (CSVI) on a remotely sensed image and the purpose is to verify the effectiveness of CSVI at the satellite-image scale. The study area was located at the Dexing Copper Mine, Jiangxi Province, China. First, the data preprocessing for the Hyperion image was conducted, including bands removal, radiometric calibration, and atmospheric correction. Second, the regions with high vegetation cover were extracted based on endmembers extraction and spectral unmixing. The CSVI was calculated on the high-vegetation-cover regions. Third, the samples of soil and leaves were collected from the study area and the copper contents in the samples were measured for the assessment. The results showed that the high values of the CSVI were near the functional regions for copper mining and the polluted rivers. What is more, there was a significant positive correlation between the CSVI calculated from the Hyperion image and the copper content in soil/leaves. We demonstrate that CSVI is applicable for monitoring the copper stress on vegetation using satellite hyperspectral images. In addition, we provide a complete example for the application of CSVI at the satellite-image scale for the first time, which is helpful for the community in remote sensing of copper-stressed vegetation.
This paper proposes a method that combined hyperspectral remote sensing with super-low-frequency (SLF) electromagnetic detection to extract oil and gas reservoir characteristics from surface to underground, for the purpose of determining oil and gas exploration target regions. The study area in Xinjiang Karamay oil–gas field, China, was investigated. First, a Hyperion dataset was used to extract altered minerals (montmorillonite, chlorite, and siderite), which were comparatively verified by field survey and spectral measurement. Second, the SLF electromagnetic datasets were then acquired where the altered minerals were distributed. An inverse distance weighting method was utilized to acquire two-dimensional profiles of the electrical feature distribution of different formations on the subsurface. Finally, existing geological data, field work, and the results derived from Hyperion images and SLF electromagnetic datasets were comprehensively analyzed to confirm the oil and gas exploration target region. The results of both hyperspectral remote sensing and SLF electromagnetic detection had a good consistency with the geological materials in this study. This paper demonstrates that the combination of hyperspectral remote sensing and SLF electromagnetic detection is suitable for the early exploration of oil and gas reservoirs, which is characterized by low exploration costs, large exploration areas, and a high working efficiency.