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

Explanatory notes to the geomorphological, geotechnical and natural hazard maps of the Hintere Bregenzerwald, vlbg. Austria

Adapting a quantitative risk assessment based method to guide land use planning decisions in India

Large scale disasters (e.g. earthquakes, tropical cyclones, and wildfires) cause wide-spread losses to buildings, damaging transportation, educational, health, industry, and agricultural infrastructure, leading to direct and indirect social and economic losses (EM_DAT,2018) . After years with large losses by earthquakes (e.g. 2008 China, 2010 Haiti, 2015 Nepal) 2017 was characterized by hurricanes and wildfires, causing a loss of 300 billion Euro (MunichRe, 2018). These events also have a large impact on the natural environment, and critically change the conditions related to vegetation, active processes and hydrology leading to new hazards or increased intensity and frequency of existing hazards (Figure 1). This aspect is often not fully considered in post-disaster reconstruction planning, leading to unfortunate new impacts and losses, and to what could be called “re-reconstruction planning”. Land-use planning is a critical action for Priority 4 of the Sendai framework “Enhancing disaster preparedness for effective response and to “Build Back Better” in recovery, rehabilitation and reconstruction” (UNISDR, 2015).

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Where to Build-Back-Better? Analyzing changing risk for post-disaster reconstruction planning

Quantitative vulnerability assessment of buildings to debris - flows in Fella River Basin using run-out modelling and damage data from the 29th of August 2003 event : extended abstract

Abstract The mapping and characterisation of building footprints is a challenging task due to inaccessibility and incompleteness of the required data, thus hindering the estimation of loss caused by natural and anthropogenic hazards. Major advancements have been made in the collaborative mapping of buildings with platforms like OpenStreetMap, however, many parts of the world still lack this information or the information is outdated. We created a semi-automated workflow for the development of elements-at-risk (EaR) databases of buildings by detecting building footprints using deep learning and characterising the footprints with building occupancy information using building morphological metrics and open-source auxiliary data. The deep learning model was used to detect building EaR footprints in a city in Kerala (India) with an F1 score of over 76%. The footprints were classified into 13 building occupancy types along with information such as average number of floors, total floor space area, building density, and percentage of built-up area. We analysed the transferability of the approach to a different city in Kerala and obtained an almost similar F1 score of 74%. We also examined the exposure of the buildings and the associated occupancies to floods using the 2018 flood susceptibility map of the respective cities. We notice certain shortcomings in our research particularly, the need for a local expert and good quality auxiliary data to obtain reasonable building occupancy information, however, our research contributes to developing a rapid method for generating a building EaR database in data-scarce regions with attributes of occupancy types, thus supporting regional risk assessment, disaster risk mitigation, risk reduction initiatives, and policy developments. 

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C.J. van Westen

Papers

Explanatory notes to the geomorphological, geotechnical and natural hazard maps of the Hintere Bregenzerwald, vlbg. Austria

Adapting a quantitative risk assessment based method to guide land use planning decisions in India

Where to Build-Back-Better? Analyzing changing risk for post-disaster reconstruction planning

Quantitative vulnerability assessment of buildings to debris - flows in Fella River Basin using run-out modelling and damage data from the 29th of August 2003 event : extended abstract

Mapping and characterising buildings for flood exposure analysis using open-source data and artificial intelligence