A study on the contribution of satellite RADAR interferometry to analyse the activity of Aso volcano (Japan)

The aim of this work is to compare the use of SAR datasets acquired by different sensors and understand which one has the best performance estimating from small to large ground displacements in volcanic area, maintaining a good spatial information. Through the reconstruction of the deformations evolution, it is possible to analyse the behaviour of the volcanic edifice before, during and after eruptions. The study is focused on Aso volcano, in the central part of the Kyushu island (Japan), which stands out for its wide caldera (18 km x 25 km). Inside the rim is included the post-caldera central cones younger than 0.1 Ma. Among 17 cones, the only crater which has been active for 80 years is Nakadake, composed by seven craterlets. In the considered time span (2007-2018), no large eruptions occurred; during its unrest period, a prevalent subsidence has persisted simultaneously with the degassing activity. Although the low intensity activity, ground displacements, detectable through remote sensing techniques, can reflect the inflate-deflate cycles of the magma chamber, situated below one of the main inactive crater (Kusasenri) at a depth of 4-5 km. Using Small Baseline Subset (SBAS) InSAR technique, ALOS Palsar-1 from 2007 to 2011, Sentinel-1 from 2014 to 2018 and ALOS Palsar-2 from 2016 to 2018 SAR datasets have been calibrated through Global Navigation Satellite System (GNSS) measurements. With the employment of SARscape software, for each time span, velocity and displacements maps have been generated to obtain deformations time series to analyse and identify the motion due to volcanic activity. An important seismic event occurred during the investigated time period is the Mw 7.0 Kumamoto earthquake happened on April 16, 2016, 60 km far from the caldera rim. Both in SAR and GPS time series was important to exclude the coseismic effect to estimate the correct trend movement due to the volcano activity. In the displacement time series, three points in correspondence of GPS located within the caldera and few points in the post-caldera central cones have been examined for each time span. Analysing the displacements time series is necessary considered that deformations are affected by many factors as geodynamic, atmospheric effects, noise, type of images processing and earthquakes further the volcanic activity and the characteristics of the sensor used for the acquisitions. The final results show that only in case of short satellite revisiting time and lesser wavelength is possible to detect low intensity activities, but sometimes using SAR data with longer revisiting time and higher wavelength helps to obtain a better spatial information in vegetated area, as in the case of Aso caldera

Assessing the importance of conditioning factor selection in landslide susceptibility for the province of Belluno (region of Veneto, northeastern Italy)

In the domain of landslide risk science, landslide susceptibility mapping (LSM) is very important, as it helps spatially identify potential landslide-prone regions. This study used a statistical ensemble model (frequency ratio and evidence belief function) and two machine learning (ML) models (random forest and XGBoost; eXtreme Gradient Boosting) for LSM in the province of Belluno (region of Veneto, northeastern Italy). The study investigated the importance of the conditioning factors in predicting landslide occurrences using the mentioned models. In this paper, we evaluated the importance of the conditioning factors in the overall prediction capabilities of the statistical and ML algorithms. By the trial-and-error method, we eliminated the least "important"features by using a common threshold of 0.30 for statistical and 0.03 for ML algorithms. Conclusively, we found that removing the least important features does not impact the overall accuracy of LSM for all three models. Based on the results of our study, the most commonly available features, for example, the topographic features, contributes to comparable results after removing the least important ones, namely the aspect plan and profile curvature, topographic wetness index (TWI), topographic roughness index (TRI), and normalized difference vegetation index (NDVI) in the case of the statistical model and the plan and profile curvature, TWI, and topographic position index (TPI) for ML algorithms. This confirms that the requirement for the important conditioning factor maps can be assessed based on the physiography of the region

Assessing the importance of feature selection in Landslide Susceptibility for Belluno province (Veneto Region, NE Italy)

In the domain of landslide risk science, landslide susceptibility mapping (LSM) is very important as it helps spatially identify potential landslide-prone regions. This study used a statistical ensemble model (Frequency Ratio and Evidence Belief Function) and two machine learning (ML) models (Random Forest and XG-Boost) for LSM in the Belluno province (Veneto Region, NE Italy). The study investigated the importance of the conditioning factors in predicting landslide occurrences using the mentioned models. In this paper, we evaluated the importance of the conditioning factors (features) in the overall prediction capabilities of the statistical and ML algorithms. By the trial-and-error method, we eliminated the least "important" features by using a common threshold. Conclusively, we found that removing the least "important" features does not impact the overall accuracy of the LSM for all three models. Based on the results of our study, the most commonly available features, for example, the topographic features, contributes to comparable results after removing the least "important" ones. This confirms that the requirement for the important factor maps can be assessed based on the physiography of the region. Based on the analysis of the three models, it was observed that most commonly available feature data can be useful for carrying out LSM at regional scale, eliminating the least available ones in most of the use cases due to data scarcity. Identifying LSMs at regional scale has implications for understanding landslide phenomena in the region and post-event relief measures, planning disaster risk reduction, mitigation, and evaluating potentially affected areas

Generating multi-temporal landslide inventories through a general deep transfer learning strategy using HR EO data

Mapping of landslides over space has seen an increasing attention and good results in the last decade. While current methods are chiefly applied to generate event-inventories, whereas multi-temporal (MT) inventories are rare, even using manual landslide mapping. Here, we present an innovative deep learning strategy which employs transfer learning that allows for the Attention Deep Supervision Multi-Scale U-Net model to be adapted for landslide detection tasks in new areas. The method also provides the flexibility of re-training a pretrained model to detect both rainfall- and earthquake-triggered landslides on new target areas. For the mapping, we used archived Planet Lab remote sensing images spanning a period between 2009 till 2021 with spatial resolution of 3–5 m to systematically generate MT landslide inventories. When we examined all cases, our approach provided an average F1 score of 0.8 indicating that we successfully identified the spatiotemporal occurrences of landslides. To examine the size distribution of mapped landslides we compared the frequency-area distributions of predicted co-seismic landslides with manually mapped products from the literature. Results showed a good match between calculated power-law exponents where the difference ranges between 0.04 and 0.21. Overall, this study showed that the proposed algorithm could be applied to large areas to generate polygon-based MT landslide inventories