Accurate landslide inventory mapping is essential for quantitative hazard and risk assessment. Although multi-temporal change detection techniques have contributed greatly to landslide inventory preparation, it is still challenging to generate quality change detection images (CDIs) for accurate landslide mapping. The recently proposed change detection-based Markov random field (CDMRF) provides an effective approach for rapid mapping of landslides with minimum user interventions. However, when CDI is generated by change vector analysis (CVA) alone, the CDMRF method may suffer from noise especially when the pre- and post-event remote sensing images are acquired under different atmospheric, illumination, and phenological conditions. This paper improved such CDMRF approach by integrating normalized difference vegetation index (NDVI), principal component analysis (PCA), and independent component analysis (ICA) generated CDIs with MRF for landslide inventory mapping from multi-sensor data. To justify the effectiveness and applicability, the improved methods were applied to map rainfall-, typhoon-, and earthquake-triggered landslides from the pre- and post-event satellite images acquired by very high resolution QuickBird, high resolution FORMOSAT-2, and moderate resolution Sentinel-2. Moreover, they were tested on pre-event Landsat-8 and post-event Sentinel-2 datasets, indicating that they are operational for landslide inventory mapping from combined multi-temporal and multi-sensor data. The results demonstrate that the improved ?NDVI-, PCA-, and ICA-based approaches perform much better than CVA-based CDMRF in terms of completeness, correctness, Kappa coefficient, and F-measures. To the best of our knowledge, it is the first time that NDVI, PCA, and ICA are integrated with MRF for landslide inventory mapping from multi-sensor data. It is anticipated that this research can be a starting point for developing new change detection techniques that can readily generate quality CDI and for applying advanced machine learning algorithms (e.g., deep learning) to automatic detection of natural hazards from multi-sensor time series data.

We propose a novel approach to evaluate the spatial and the temporal distribution of societal landslide risk from historical, sparse, point information on fatal landslides and their direct human consequences. We test the ap- proach using a record of 5571 fatalities caused by 1017 landslides at 958 sites across Italy, in the 155-year period 1861-2015. Adopting a Zipf distribution, we model societal landslide risk for the whole of Italy, and for seven physiographic and 20 administrative subdivisions of Italy. Results confirm that the Zipf distribution is adequate to describe the frequency (and the probability) of fatal landslides, and show that societal landslide risk varies in Italy depending on the largest magnitude landslide F, the number of fatal events E, and the scaling exponent of the Zipf distribution s, which controls the relative proportion of low vs. large magnitude landslides. To model societal landslide risk, we then test different grid spacings, g and circular kernel sizes, r finally adopting g = 10 km and r = 55 km. Using such geometrical constraints, we prepare maps of the variables F, E and s, revealing the complexity of landslide risk in Italy, which cannot be described properly with a single metric. For each grid cell, we assign the {F, E, s} variables to the red, green and blue bands of a composite image to obtain a single view of landslide risk to the population of Italy. Next, we prepare risk scenarios for landslides of increasing magnitudes, which we validate checking the anticipated return period of the fatal events against information on 130 fatal landslides between 1000 and 1860, and eleven fatal landslides between January 2016 and August 2018. Despite incompleteness in the old part of the record for the low magnitude landslides, and the short length and limited number of events in the recent period 2016-2018, the anticipated return periods are in good agreement with the occurrence of fatal landslides in both validation periods. Despite the known difficulty in modelling sparse datasets, the approach provided a coherent and realistic representation of societal landslide risk in Italy. Our results give new insight on the spatial and temporal variations of societal landslide risk in Italy. We expect this to contribute to improve existing zonings of landslide risk in Italy; to foster the efficacy of national and regional landslide early warning systems; and to design and implement better landslide commu- nication, mitigation and adaptation strategies. Our approach is general and not constrained to the information on fatal landslides available for Italy. We therefore expect the approach to be used to model societal landslide risk in other geographical areas for which adequate information is available, and to model the fatal consequences of other hazards.

We present a geotechnical map of Gran Canaria (Canary Islands, Spain) based on the laboratory and field characterization of 344 samples collected in the main 7 islands of the Archipelago. The map is obtained grouping the 109 lithologies of the official geological map into 7 geotechnical classes, taking into account the value of the uniaxial compressive strength of the intact rock. The new map shows a majority of hard and very hard rocks, which is consistent with the intra-plate volcanic geology of the island. As an application of the geotechnical map, and taking into account that rockfalls represent a severe threat in Gran Canaria, we develop a rockfall-runout map by applying STONE, a three-dimensional simulation program that generates simple colour maps useful to assess rockfall hazard. The accuracy of the simulation depends on the appropriate identification of the friction and energy restitution coefficients, which are derived from the geotechnical map and calibrated using two well-known rockfall cases occurred in the island. The runout map provides useful information at regional scale to evaluate and mitigate rockfall hazards, and represent a replicable application in other volcanic environments.

Long-term gridded precipitation products are crucial for several applications in hydrology, agriculture and climate sciences. Currently available precipitation products obtained from rain gauges, remote sensing and meteorological modelling suffer from space and time inconsistency due to non-uniform density of ground networks and the difficulties in merging multiple satellite sensors. The recent bottom up approach that uses satellite soil moisture observations for estimating rainfall through the SM2RAIN algorithm is suited to build long-term and consistent rainfall data record as a single polar orbiting satellite sensor is used. We exploit here the Advanced SCATterometer (ASCAT) on board three Metop satellites, launched in 2006, 2012 and 2018. The continuity of the scatterometer sensor on European operational weather satellites is ensured until mid-2040s through the Metop Second Generation Programme. By applying SM2RAIN algorithm to ASCAT soil moisture observations a long-term rainfall data record can be obtained, also operationally available in near real time. The paper describes the recent improvements in data pre-processing, SM2RAIN algorithm formulation, and data post-processing for obtaining the SM2RAIN-ASCAT global daily rainfall dataset at 12.5 km sampling (2007-2018). The quality of SM2RAIN-ASCAT dataset is assessed on a regional scale through the comparison with high-quality ground networks in Europe, United States, India and Australia. Moreover, an assessment on a global scale is provided by using the Triple Collocation technique allowing us also the comparison with other global products such as the latest European Centre for Medium-Range Weather Forecasts reanalysis (ERA5), the Global Precipitation Measurement (GPM) mission, and the gauge-based Global Precipitation Climatology Centre (GPCC) product. Results show that the SM2RAIN-ASCAT rainfall dataset performs relatively well both at regional and global scale, mainly in terms of root mean square error when compared to other datasets. Specifically, SM2RAIN-ASCAT dataset provides better performance better than GPM and GPCC in the data scarce regions of the world, such as Africa and South America. In these areas we expect the larger benefits in using SM2RAIN-ASCAT for hydrological and agricultural applications. The SM2RAIN-ASCAT dataset is freely available at https_//doi.org/10.5281/zenodo.2591215.