Synthetic aperture radar interferometry is an imaging technique for measuring the topography of a surface, its changes over time, and other changes in the detailed characteristic of the surface. By exploiting the phase of the coherent radar signal, interferometry has transformed radar remote sensing from a largely interpretive science to a quantitative tool, with applications in cartography, geodesy, land cover characterization, and natural hazards. This paper reviews the techniques of interferometry, systems and limitations, and applications in a rapidly growing area of science and engineering.

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Synthetic aperture radar interferometry

Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy

Landsliding in response to rainfall involves physical processes that operate on disparate timescales. Relationships between these timescales guide development of a mathematical model that uses reduced forms of Richards equation to evaluate effects of rainfall infiltration on landslide occurrence, timing, depth, and acceleration in diverse situations. The longest pertinent timescale is A/D0, where D0 is the maximum hydraulic diffusivity of the soil and A is the catchment area that potentially affects groundwater pressures at a prospective landslide slip surface location with areal coordinates x, y and depth H. Times greater than A/D0 are necessary for establishment of steady background water pressures that develop at (x, y, H) in response to rainfall averaged over periods that commonly range from days to many decades. These steady groundwater pressures influence the propensity for landsliding at (x, y, H), but they do not trigger slope failure. Failure results from rainfall over a typically shorter timescale H2/D0 associated with transient pore pressure transmission during and following storms. Commonly, this timescale ranges from minutes to months. The shortest timescale affecting landslide responses to rainfall is H/g, where g is the magnitude of gravitational acceleration. Postfailure landslide motion occurs on this timescale, which indicates that the thinnest landslides accelerate most quickly if all other factors are constant. Effects of hydrologic processes on landslide processes across these diverse timescales are encapsulated by a response function, R(t*) = t*/π exp (−1/t*) − erfc (1/t*), which depends only on normalized time, t*. Use of R(t*) in conjunction with topographic data, rainfall intensity and duration information, an infinite-slope failure criterion, and Newton's second law predicts the timing, depth, and acceleration of rainfall-triggered landslides. Data from contrasting landslides that exhibit rapid, shallow motion and slow, deep-seated motion corroborate these predictions.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2000WR900090

Landslide triggering by rain infiltration

A copy of Guangbo jiemu bao [Broadcast Program Report] was being passed from hand to hand among a group of young people eager to be the first to read the article introducing the program "What Is Revolutionary Love?" It said: "… Young friends, you are certainly very concerned about this problem'. So, we would like you to meet the young women workers Meng Xiaoyu and Meng Yamei and the older cadre Miss Feng. They are the three leading characters in the short story ‘The Place of Love.’ Through the description of the love lives of these three, the story induces us to think deeply about two questions that merit further examination.

This is How the Discussion Started

The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan

In this exercise the simplified RADIUS methodology is used to prepare a seismic hazard microzonation map for the city of Kathmandu, Nepal.

Application of GIS for Earthquake Hazard and Risk Assessment: Kathmandu, Nepal

Stakeholders in disaster risk management are faced with the challenge to adapt their risk reduction policies and emergency plans to cascading and compounding events, but often lack the tools to account for the cross-sectoral impacts and dynamic nature of the risks involved. The EU Horizon Europe PARATUS project, which started in October 2022 and will run to October 2026, aims to fill this gap by developing an open-source online platform for dynamic risk assessment that allows to analyze and evaluate multi-hazard impact chains, dynamic risk reduction measures, and disaster response scenarios in the light of systemic vulnerabilities and uncertainties. These services will be co-developed within a transdisciplinary consortium of 19 partners, consisting of research organizations, NGOs, SMEs, first and second responders, and local and regional authorities. To gain a deeper understanding of multi-hazard impact chains, PARATUS conducts forensic analysis of historical disaster events, based on a database of learning case studies, augments historical disaster databases with hazard interactions and sectorial impacts, and exploits remote sensing data with artificial intelligence methods. Building on these insights, PARATUS will then develop new exposure and vulnerability analysis methods that enable systemic risk assessment across sectors (e.g. humanitarian, transportation, communication) and geographic settings (e.g. islands, mountains, megacities). These methods will be used to analyze risk changes across space and time and to develop new scenarios and risk mitigation options together with stakeholders, using innovative serious games and social simulations.The methods developed in PARATUS have been applied in four application case studies. The first one is related to Small Island Developing States (SIDS) in the Caribbean. This case study considers the cross-border impacts of tropical storms, tsunamis, volcanic eruptions, and space weather, and focuses on the development of impact-based forecasting, directed at humanitarian response planning, the telecommunication sector, and tourism. The second case study deals with the local and regional economic impact of hazardous events such as extreme wind, floods, rockfall, mudflow, landslides, and snow avalanches on cross-border transportation in the Alps. The third case study relates to the multi-hazard impact of large earthquakes in the Bucharest Metropolitan Region and focuses on systemic vulnerabilities of the city and emergency response. The fourth application case study is the Megacity of Istanbul which is prone to earthquake hazard chains, such as liquefaction, landslides, and tsunami, as well as to hydrometeorological hazards (extreme temperatures, fires, and flooding). Population growth rates, urban expansion speed, composition, and integration of new migrants (native, foreign, and refugees from countries like Syria and Afghanistan) contribute to the increasing disaster risk.&amp;#160;The project results will be hosted on two stakeholder hubs related to crisis management and humanitarian relief, and provide stakeholders with a set of tools for risk reduction planning in dynamic multi-hazard environments. The service-oriented approach with active stakeholder involvement will maximize the uptake and impact of the project, and help to increase Europe&amp;#8217;s resilience to compounding disasters. 

Promoting disaster preparedness and resilience by co-developing stakeholder support tools for managing the systemic risk of compounding disasters

Shallow landslides are frequently occurring hazards in mountainous landscapes all over the world. These processes are caused by a combination of static (i.e., predisposing factors: topography, material properties) and dynamic controls (i.e., preparatory and triggering factors: heavy rainfall, snow-melt). Data-driven methods have been used to model shallow landslides at regional scales, in which efforts have been taken to separately investigate the spatial component (i.e., landslide susceptibility) and temporally-varying conditions (e.g., rainfall thresholds). However, the joint assessment of shallow landslides in space and time using data-driven methods remains challenging.In the present work, we aim to predict the occurrence of precipitation-induced shallow landslides in space and time (i.e., the where and the when) within the Italian province of South Tyrol (7,400 km&amp;#178;). In this context, we test the added value of describing the precipitation leading to landslide occurrence as a functional predictor, in contrast to traditional approaches where precipitation is taken as a scalar predictor. We built upon hourly precipitation data from the Integrated Nowcasting through Comprehensive Analysis system (INCA, provided by Geosphere Austria) and past landslide occurrences from 2000 to 2021, which systematically relate to damage-causing landslide events. The methodical framework comprised filtering the landslide inventory, sampling landslide absences in space and time (i.e., balanced across years and months), extracting static and dynamic environmental factors (e.g., topography, lithology, land cover, and hourly precipitation), and removing trivial areas and time periods. We implemented a Functional Generalized Additive Model (FGAM) to derive statistical relationships between the different static factors as scalar predictors, the hourly precipitation preceding a potential landslide event as a functional predictor, and the occurrence in space and time of shallow landslides. The resulting predictions were assessed using cross-validation and transferred into space for different precipitation measures in order to hindcast landslide events.The results from this novel approach are expected to integrate landslide predictions in space and time for large areas by accounting for static and dynamic (i.e., hourly precipitation grids) landslide controls, seasonal effects, and the underlying data limitations (e.g., inventory incompleteness). The findings associated with this research are framed within the PROSLIDE project, which has received funding from the research program Research S&amp;#252;dtirol/Alto Adige 2019 of the Autonomous Province of Bozen/Bolzano &amp;#8211; S&amp;#252;dtirol/Alto Adige. 

Functional regression for space-time prediction of precipitation-induced shallow landslides in South Tyrol, Italy

Fast and accurate landslide detection is important for landslide early warning systems. However, data available from local authorities and news reports vary in accuracy (time and location). In this work, we present a new method for identifying landslides, based on Google Earth Engine (GEE) and time-series analysis of Sentinel-2 optical satellite images. The method uses vegetation loss as a proxy for disturbance caused by earthquake-related landslides, and applies a change detection algorithm to compute the Normalized Different Vegetation Index (NDVI) and Relative Different NDVI (rdNDVI). As a test case, we applied this approach to the area of Palu, Central Sulawesi, which was hit by a major earthquake on September 28, 2018. Using time series data from 2015 to 2020, we were able to accurately capture the massive landslide in Poi Village caused by this earthquake. Using GEE had many advantages: the process is semi-automatic, fast and versatile, and the boundaries of the landslide zones can be auto-generated. In addition, the analysis does not require expensive high-resolution data. Our results demonstrate the potential of this new method to produce landslide inventories in a fast, accurate and low-cost manner.

Semi-automatic Landslide Detection Using Google Earth Engine, a Case Study in Poi Village, Central Sulawesi

Determining the location of coseismic landslides damaged buildings is critical to the understanding of human losses from earthquake-induced landslides during major earthquakes in mountainous regions. Because of the vast area of the earthquake affected region and the sparse locations of rural settlements, previous knowledge on buildings and human losses resulting from coseismic landslides mainly rely on rough estimations from limited field work. Application of high resolution satellite remote sensing opens a new way to map pre-seismic rural mountainous housing over large areas, and thus can accurately determine the location of coseismic landslide damaged housing spatially. In this work, we used the 2008 Wenchuan Earthquake as an example and use SPOT5 images taken before the earthquake for a seimi-automated mapping of buildings in rural areas. Then, by spatial overlay analysis with an existing coseismic landslide inventory, we mapped the location of landslide damaged buildings in this area. This semi-automatic method can be further applied to the entire Wenchuan Earthquake affected region to determine the locations of all landslides damaged buildings and finally improve the performance of earthquake loss estimation models.

C.J. van Westen

Papers

Application of GIS for Earthquake Hazard and Risk Assessment: Kathmandu, Nepal

Promoting disaster preparedness and resilience by co-developing stakeholder support tools for managing the systemic risk of compounding disasters

Functional regression for space-time prediction of precipitation-induced shallow landslides in South Tyrol, Italy

Semi-automatic Landslide Detection Using Google Earth Engine, a Case Study in Poi Village, Central Sulawesi

Mapping coseismic landslide damaged buildings during the 2008 Wenchuan Earthquake in rural mountainous area