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A supply of minerals is critical to socioeconomic development. However, such a supply also induces negative impacts on environment and ecology, e.g., leading to dust emission and deposition. An ultra-low-grade magnetite has been exploited as a new iron type since 2001 in China. In this paper, two Landsat images were used for monitoring foliar dust in Changhe River Mining Area, China. First, models were established to estimate foliar dust using vegetation indices (VIs) differences according to laboratory spectral measurements; normalized differenced VI was selected as an optimal VI for estimating foliar dust amount based on both field and laboratory spectral measurements (RMSE = 6.58 g/m2), and finally, the spatial patterns of foliar dust were analyzed by using ancillary high-resolution data. The result showed that most foliar dust distributed near ore transportation roads and around mining sites and tailings ponds, which was related to ultra-low-grade characteristics of the iron ore due to large-area extraction and tailings occupation, and large-amount dust emission released from ore transportation. The remote sensing method for estimating foliar dust may be beneficial for environmental management in mining areas.

In this paper, we proposed a wavelet-based methodology that enables to detect and diagnose the ionospheric anomalies associated with seismic activity. Wavelet transform and cross-wavelet analysis methods are used to detect and identify the spatio-temporal variability of ionospheric perturbation associated with Wenchuan Ms8.0 earthquake, 12 May 2008 in China, and to gain further insights into the dynamical relations between the ionospheric anomaly variability and the geophysical indices variability in the same span of observations, 12 April ∼ 12 May, 2008. The details of the algorithm and the analysis results are introduced in this paper.

The satellite remote sensing has become a promising technique for detecting earthquake and fault activities. But the radiation and the reflection of the earth surface detected by remote sensing are influenced not only by earthquake or fault activities but also by many factors such as terrain, vegetation, weather and so on. In order to effectively extract the earthquake anomaly and get rid of the impacts of non-earthquake factors, this paper presented a new method to extract and analyze anomaly caused by earthquakes or fault activities. We applied the method to the data of AMSR-E and MODIS for the earthquake that struck Sichuan, China, on May 12, 2008. As a result, we detected that the thermal infrared (TIR) anomaly information retrieved from MODIS data emerged before the earthquake and the microwave radiation anomaly retrieved from AMSR-E data emerged both before and after the earthquake.