The particular process of LCA (Lithosphere-Coversphere-Atmosphere) coupling referring to local tectonic structures and coversphere conditions is very important for understanding seismic anomaly from GEOSS (Global Earth Observation System of Systems). The LCA coupling based multiple-parameters analysis should be the foundation for earthquake prewarning. Three improved criterions: deviation notable enough, time quasi-synchronism, and space geo-adjacency, plus their quantify indices are defined for earthquake anomaly recognition, and applied to thermal parameters as a DTS-T (Deviation-Time-Space-Thermal) method. A normalized reliability index is preliminarily defined considering three quantify indices for deviation-time-space criterions. As an example, the DTS-T method is applied to the Ms 7.1 Yushu earthquake of 14 April 2010 in China. Furthermore, combining with the previous analysis of six recent significant earthquakes in the world, the statistical results regarding effective parameters, the occurrence of anomaly before the main shocks and a reliability index for each earthquake are introduced. It shows that the DTS-T method is reasonable and can be applied for routine monitoring and prewarning in the tectonic seismicity region.

Soil nutrient is one of the most important properties for improving farmland quality and product. Imaging spectrometry has the potential for rapid acquisition and real-time monitoring of soil characteristics. This study aims to explore the preprocessing and modeling methods of hyperspectral images obtained from an unmanned aerial vehicle (UAV) platform for estimating the soil organic matter (SOM) and soil total nitrogen (STN) in farmland. The results showed that: (1) Multiplicative Scattering Correction (MSC) performed better in reducing image scattering noise than Standard Normal Variate (SNV) transformation or spectral derivatives, and it yielded a result with higher correlation and lower signal-to-noise ratio; (2) The proposed feature selection method combining Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling algorithm (CARS), could provide selective preference for hyperspectral bands. Exploiting this method, 24 and 22 feature bands were selected for SOM and STN estimation, respectively; (3) The particle swarm optimization (PSO) algorithm was employed to obtain optimized input weights and bias values of the extreme learning machine (ELM) model for more accurate prediction of SOM and STN. The improved PSO-ELM model based on the selected preference bands achieved higher prediction accuracy (R² of 0.73 and RPD of 1.91 for SOM, R² of 0.63, and RPD of 1.53 for STN) than support vector machine (SVM), partial least squares regression (PLSR), and the ELM model. This study provides an important guideline for monitoring soil nutrient for precision agriculture with imaging spectrometry.

The additional microwave radiation from underground rock mass (lithosphere) that is caused by tectonic activity and alternating crustal stress has been shown to be a detectable electromagnetic signal via satellite remote sensing. However, the emission of microwave radiation produced inside the lithosphere will be affected by its overburden matter, such as sand, soil, water, and vegetation, and this effect is unknown. In this article, we use a C-band microwave radiometer to detect the variations in the microwave brightness temperature (TB) of rock samples in the process of axial loading. For the rock samples, the surface was bare and covered with dry sand or humid sand. The experimental detection illustrates that the dry sand unexpectedly allows more stress-associated additional radiation (Δ TB) to be received by the radiometer, while humid sand shows significant extinction of Δ TB. Both the radiative transfer theory and the stress-activated positive hole hypothesis are applied to interpret the mechanisms of the effect of dry and humid sand layers on Δ TB. The outflow of stress-activated positive holes is from inside the loaded rock, and hence their accumulation beneath the surface of the sand layer is expected to reduce the local dielectric permittivity of sand, thereby making the detected Δ TB by the microwave radiometer to be larger than that extrapolated theoretically in accordance with radiative transfer theory. This article is valuable for understanding the diverse TB anomalies that have been observed prior to tectonic earthquakes, and it facilitates the evaluation of the potential application of Δ TB to seismic monitoring and earthquake prediction.

Geological disasters are responsible for the loss of human lives and for significant economic and financial damage every year. Considering that these disasters may occur anywhere—both in remote and/or in highly populated areas—and anytime, continuously monitoring areas known to be more prone to geohazards can help to determine preventive or alert actions to safeguard human life, property and businesses. Remote sensing technology—especially satellite-based—can be of help due to its high spatial and temporal coverage. Indeed, data acquired from the most recent satellite missions is considered suitable for a detailed reconstruction of past events but also to continuously monitor sensitive areas on the lookout for potential geohazards. This work aims to apply different techniques and methods for extensive exploitation and analysis of remote sensing data, with special emphasis given to landslide hazard, risk management and disaster prevention. Multi-temporal SAR (Synthetic Aperture Radar) interferometry, SAR tomography, high-resolution image matching and data modelling are used to map out landslides and other geohazards and to also monitor possible hazardous geological activity, addressing different study areas: (i) surface deformation of mountain slopes and glaciers; (ii) land surface displacement; and (iii) subsidence, landslides and ground fissure. Results from both the processing and analysis of a dataset of earth observation (EO) multi-source data support the conclusion that geohazards can be identified, studied and monitored in an effective way using new techniques applied to multi-source EO data. As future work, the aim is threefold: extend this study to sensitive areas located in different countries; monitor structures that have strategic, cultural and/or economical relevance; and resort to artificial intelligence (AI) techniques to be able to analyse the huge amount of data generated by satellite missions and extract useful information in due course.

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.

Previous studies have shown that thermal infrared radiation (TIR) anomalies occur in the vicinity of fractures that form when a rock is loaded to failure. Different types of fracturing modes correspond to different TIR anomaly trends. However, the spectral features and the mechanisms responsible for the TIR changes in the fracturing stage remain poorly understood. In this paper, experiments involving observations of the thermal infrared spectrum (8.0–13.0 μm) of loaded sandstone during the fracturing stage were conducted under outdoor conditions. The experiment yielded the following results: (1) Different fracturing modes can lead to different trends in the spectral radiance variation; (2) when an extensional fissure appeared on the rock surface, the radiance increased with a local peak in the 8.0–9.7 μm range; (3) when local bulging formed at the surface, the radiance decreased, with a local valley in the 8.0–9.7 μm range. The radiance variation caused by morphologic changes is the combined result of changes in both the temperature and the emissivity. The characteristic waveband corresponding to the reststrahlen features (RF) of quartz was mainly related to the emissivity change. This study provides a preliminary experimental foundation for the detection of crustal surface fractures via satellite-based remote sensing technology.