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.

Monitoring and assessing coastal subsidence is crucial to mitigating potential disaster risks associated with rising sea levels. Nansha District in Guangzhou City, representing global coastal soft-soil urban areas, faces significant challenges related to ground subsidence. However, the current understanding of the status, causative factors, and risk (includes subsidence susceptibility and vulnerability) assessment of ground subsidence in Nansha District is unclear. To address this gap, we utilized the SBAS-InSAR technique, analyzing 49 Sentinel-1A images from December 2015 to June 2019, for systematic ground subsidence monitoring. Subsequently, we assessed subsidence risk using a comprehensive index method and a risk matrix. Our findings indicate that subsidence velocity primarily ranged from −40 to −5 mm/a, with a spatial pattern of increasing subsidence from inland to coastal areas. The cumulative subsidence process unfolded in four distinct stages. The genesis of land subsidence was linked to an endogenous geological context dominated by soft-soil deposition, influenced by external factors such as surface loading and groundwater extraction. High-risk zones were concentrated in key engineering development areas, transportation pipeline trunk lines, and densely populated regions, demanding special attention. This study provides a foundational resource for disaster prevention and control strategies in Nansha District and similar coastal cities.

Changbaishan volcano (China/North Korea border) was responsible for the largest eruption of the first Millennium in 946 CE and is characterized by a multi-level plumbing system. It last erupted in 1903 and presently consists of a cone with summit caldera. An unrest episode occurred between 2002 and 2006, followed by subsidence. Here, we analyze the Changbaishan 2018–2020 deformations by using remote sensing data, detecting an up to 20 mm/yr, NW-SE elongated, Line of Sight movement of its southeastern flank and a −20 mm/yr Line of Sight movement of the southwestern flank. This reveals an unrest occurring during 2018–2020. Modeling results suggest that three active sources are responsible for the observed ground velocities: a deep tabular deflating source, a shallower inflating NW-SE elongated spheroid source, and a NW-SE striking dip-slip fault. The depth and geometry of the inferred sources are consistent with independent petrological and geophysical data. Our results reveal an upward magma migration from 14 to 7.7 km. The modeling of the leveling data of the 2002–2005 uplift and 2009–2011 subsidence depicts sources consistent with the 2018–2020 active system retrieved. The past uplift is interpreted as related to pressurization of the upper portion of the spheroid magma chamber, whereas the subsidence is consistent with the crystallization of its floor, this latter reactivated in 2018–2020. Therefore, Changbaishan is affected by an active magma recharge reactivating a NW-SE trending fault system. Satellite data analysis is a key tool to unravel the magma dynamics at poorly monitored and cross-border volcanoes.