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The original discovery of active submarine landslides and turbidity currents in the deep ocean was made in the 1950s through analysis of breaks in transoceanic communications cables. Further insights regarding the causes, frequency, and behavior of damaging submarine flows are presented here, based on recent disruptions of modern communications cables in the Strait of Luzon off southern Taiwan. In 2006, the Pingtung earthquake triggered landslides and at least three sediment density flows (a general term covering turbidity currents and similar flows). These flows sped down submarine canyons and into the Manila Trench at 12.7–5.6 m s⁻¹ (45–20 km h⁻¹), resulting in 22 cable breaks. In 2009, the cables were again damaged, this time by extreme river discharge associated with Typhoon Morakot. Two cables were damaged during the main flood when debris-charged river waters dived to the seabed and down Gaoping Canyon. A second, more damaging sediment density flow formed three days later when river levels were near normal and seismic activity was low. It is suggested that this second flow resulted from deposited flood sediment that was remobilized possibly by internal wave activity. Further breaks were reported in 2010 and 2012. While historical cable break databases are incomplete, they imply that since at least 1989, density flows capable of breaking cables have been infrequent, but they increased markedly after the 2006 Pingtung earthquake—a time that coincided with a transition to more extreme rainfall associated with northward migration of typhoon tracks to Taiwan.

Geohazards pose a serious and widespread threat to regional vegetation. However, the existing studies focused on the vegetation changes under climate change and human activities, but largely ignored the impact of geohazards on vegetation. To fill this gap, time series geohazards, leaf area index (LAI), vegetation net primary productivity (NPP), and the high-precision land use/land cover data were used to explore the spatiotemporal dynamics of geohazards and vegetation in China’s Sichuan Province from 2000 to 2020. More importantly, a new conceptual framework was proposed to quantify the impact of geohazards on regional vegetation from area and growth status. The results indicate that since 2000, the number of geohazards (NGH) and the density of geohazards (DGH) showed a sharp upward trend in the study area. Another important finding was that geohazards have seriously affected the transfer direction and area of vegetation; every 1% increase in DGH will lead to a net loss of 8.76 km2 area of vegetation. Moreover, geohazards hinder the growth of vegetation. Specifically, an increase of 0.01/km2 in DGH caused 0.30% and 0.11% decrease in LAI and NPP in the 1000–2000 m buffer zone, whereas 0.19% and 0.08% decrease in the 2000–3000 m buffer zone. The findings contribute to a better understanding of the response mechanisms and uncertainties of vegetation to geohazards.

Currently, Global Navigation Satellite System (GNSS) Real-Time Kinematic positioning (RTK) and Precise Point Positioning (PPP) techniques are widely employed for real-time monitoring of landslides. However, both RTK and PPP monitoring techniques have their limitations, such as limited service coverage or long convergence times. PPP-RTK technique which integrates RTK and PPP is a novel approach for monitoring landslides with the advantages of rapid convergence, high-precision, and a wide service area. This study summarizes the limitations of RTK, PPP, and PPP-RTK monitoring techniques and suggests some improved strategies. Their performances are compared and analyzed using real monitoring data. The experiment results demonstrate that RTK is the best option for small-scale (the baseline distance < 15 km) and real-time landslide monitoring without considering the cost. PPP technique converges to centimeter-level accuracy in tens of minutes, only suitable for the stability analysis of reference stations. Over a large area (the baseline distance < 100 km), PPP-RTK can provide excellent horizontal accuracy and adapt the service range in response to the demand for monitoring accuracy, as the vertical accuracy is significantly impacted by the service range and elevation difference. Finally, the characteristics of three techniques are integrated to form a comprehensive landslide monitoring technique that considers intelligence, robustness, and real-time.

Geohazards such as collapses, landslides, and debris flows result from complex interactions between human activities and environmental conditions. However, a quantitative understanding of their coupling mechanisms remains challenging. This study developed a machine learning-based classification framework​ for multi-geohazard susceptibility mapping (GSM) in Zhejiang Province, China, to address this gap. The study employed XGBoost, AdaBoost, and Random Forest to construct individual models for each geohazard, using a comprehensive set of geomorphologic, geological, environmental, hydrological, and anthropogenic factors. The XGBoost model achieved Area Under the Curve (AUC) values greater than 0.9 for all geohazards, and was selected as the optimal model for GSM. Results show: (1) Topographic position index (TPI) and distance to roads are the most influential factors, with dominant roles varying by geohazard—TPI primarily controls debris flows, while collapses are more driven by road proximity. (2) Anthropogenic factors account for 15.9%–33.8% of importance across geohazards. (3) The dependence plots and heatmap of interaction values reveal the impact of human–natural factor coupling mechanisms on geohazards. The study provides a quantitative and interpretable analysis of human–natural environment coupling, offering insights for risk management and spatial planning in densely populated coastal regions under climate change.

We are communicating recent developments regarding the Surface motioN mAPPING (SNAPPING) service for the Sentinel-1 mission on the Geohazards Exploitation Platform (GEP) platform in support of the scientific community as well as of EO practitioners. We present the processing scheme adopted for the service and the designed implementation on the GEP, and we discuss in detail the user-defined processing parameters and service outputs. SNAPPING is offered through three independent services, namely the SNAPPING IFG for the generation of interferometric stacks, utilized consequently as input for the SNAPPING PSI Med and SNAPPING PSI Full services, which execute Persistent Scatterers Interferometry (PSI) analyses at medium and full resolutions, respectively. The inter-verification of the SNAPPING results was performed to underline the robustness of the provided measurements, and several showcases from diverse environments are demonstrated. The service aims to pave the way towards the improved acceptance of EO-hosted processing services and deeper community engagement, anticipating operational exploitation in response to geohazards.

Global Navigation Satellite System (GNSS) has drawn the attention of scientists and users all over the world for its wide-ranging Earth observations and applications. Since the end of May 2022, more than 130 satellites are available for fully global operational satellite navigation systems, such as BeiDou Navigation Satellite System (BDS), Galileo, GLONASS and GPS, which have been widely used in positioning, navigation, and timing (PNT), e.g., precise orbit determination and location-based services. Recently, the refracted, reflected, and scattered signals from GNSS can remotely sense the Earth’s surface and atmosphere with potential applications in environmental remote sensing. In this paper, a review of multi-GNSS for Earth Observation and emerging application progress is presented, including GNSS positioning and orbiting, GNSS meteorology, GNSS ionosphere and space weather, GNSS-Reflectometry and GNSS earthquake monitoring, as well as GNSS integrated techniques for land and structural health monitoring. One of the most significant findings from this review is that, nowadays, GNSS is one of the best techniques in the field of Earth observation, not only for traditional positioning applications, but also for integrated remote sensing applications. With continuous improvements and developments in terms of performance, availability, modernization, and hybridizing, multi-GNSS will become a milestone for Earth observations and future applications.

The wireless sensor network (WSN) technology has applied in monitoring natural disasters for more than one decade. Disasters can be closely monitored by augmenting a variety of sensors, and WSN has merits in (1) low cost, (2) quick response, and (3) salability and flexibility. Natural disaster monitoring with WSN is a well-known data intensive application for the high bandwidth requirements and stringent delay constraints. It manifests a typical paradigm of data-intensive application upon low-cost scalable system. In this study, we first assessed representative works in this area by classifying those in the domains of application of WSNs for disasters and optimization technologies significantly distinguishing these from general-purpose WSNs. We then described the design of an early warning system for geohazards in reservoir region, which relies on the WSN technology inspired by the existing work with focuses on issues of (1) supporting reliable data transmission, (2) handling huge data of heterogeneous sources and types, and (3) minimizing energy consumption. This study proposes a dynamic routing protocol, a method for network recovery, and a method for managing mobile nodes to enable real-time and reliable data transmission. The system incorporates data fusion and reconstruction approaches to bring together all data into a single view of the geohazard under monitoring. A distributed algorithm for joint optimal control of power and rate has been developed, which can improve utility of network (> 95 %) and to minimize the energy consumption (reduction by > 20 % in comparison with LEACH). Experimental results indicate the potentials of the proposed approaches in terms of adapting to the needs of early warning on geohazards.

The Central Valley in California (USA) covers about 52,000 km 2 and is one of the most productive agricultural regions in the world. This agriculture relies heavily on surface-water diversions and groundwater pumpage to meet irrigation water demand. Because the valley is semi-arid and surface-water availability varies substantially, agriculture relies heavily on local groundwater. In the southern two thirds of the valley, the San Joaquin Valley, historic and recent groundwater pumpage has caused significant and extensive drawdowns, aquifer-system compaction and subsidence. During recent drought periods (2007–2009 and 2012-present), groundwater pumping has increased owing to a combination of decreased surface-water availability and land-use changes. Declining groundwater levels, approaching or surpassing historical low levels, have caused accelerated and renewed compaction and subsidence that likely is mostly permanent. The subsidence has caused operational, maintenance, and construction-design problems for water-delivery and flood-control canals in the San Joaquin Valley. Planning for the effects of continued subsidence in the area is important for water agencies. As land use, managed aquifer recharge, and surface-water availability continue to vary, long-term groundwater-level and subsidence monitoring and modelling are critical to understanding the dynamics of historical and continued groundwater use resulting in additional water-level and groundwater storage declines, and associated subsidence. Modeling tools such as the Central Valley Hydrologic Model, can be used in the evaluation of management strategies to mitigate adverse impacts due to subsidence while also optimizing water availability. This knowledge will be critical for successful implementation of recent legislation aimed toward sustainable groundwater use.

Two successive landslides within a month started in October 11, 2018, and dammed twice the Jinsha River at the border between Sichuan Province and Tibet in China. Both events had potential to cause catastrophic flooding that would have disrupted lives of millions and induced significant economic losses. Fortunately, prompt action by local authorities supported by the deployment of a real-time landslide early warning system allowed for quick and safe construction of a spillway to drain the dammed lake. It averted the worst scenario without loss of life and property at least one order of magnitude less to what would have been observed without quick intervention. Particularly, the early warning system was able to predict the second large-scale slope failure 24 h in advance, along with minor rock falls during the spillway construction, avoiding false alerts. This paper presents the main characteristics of both slope collapses and damming processes, and introduces the successful landslide early warning system. Furthermore, we found that the slope endured cumulative creeping displacements of > 40 m in the past decade before the first event. Twenty-five meter displacement occurred in the year immediately before. The deformation was measured by the visual interpretation of multitemporal satellite images, which agrees with the interferometry synthetic aperture radar (InSAR) measurement. If these had been done before the emergency, economic losses could have been reduced further. Therefore, our findings strengthen the case for the deployment of systematic monitoring of potential landslide sites by integrating earth observation methods (i.e., multitemporal satellite or UAV images) and in situ monitoring system as a way to reduce risk. It is expected that this success story can be replicated worldwide, contributing to make our society more resilient to landslide events.

Fifty-five descending images from the ENVISAT satellite were processed using the small baseline subset (SBAS) method to derive the spatial and temporal ground deformation of the Bailong River Basin between 2003 and 2010. The basin is one of the most severely landslide- and debris flow-affected areas of China. As a result, 104 sites with high deformation areas were identified. Interferometric Synthetic Aperture Radar (InSAR) analysis was combined with landslide inventory data and field surveys, and anomalous areas were classified into three main types: landslide; debris; and subsidence. Displacement rates up to 35 mm/yr were evaluated away from the sensor along a line-of-sight (LOS) direction. The results gained should allow a more accurate prediction and monitoring of landslides, debris, and subsidence; further, they demonstrate the capability of the SBAS method to analyze any displacement effect and identify dangerous and uninhabitable areas in the basin. The small baseline subset method can thus contribute to the prediction and prevention of geohazards in the area.