ALBERT

Description: The increasing availability of free-access satellite data represents a relevant opportunity for the analysis and assessment of natural hazards. The systematic acquisition of spaceborne imagery allows for monitoring areas prone to geohydrological disasters, providing relevant information for risk evaluation and management. In cases of major landslide events, for example, spaceborne radar data can provide an effective solution for the detection of slope failures, even in cases with persistent cloud cover. The information about the extension and location of the landslide-affected areas may support decision-making processes during emergency responses. In this paper, we present an automatic procedure based on Sentinel-1 Synthetic Aperture Radar (SAR) images, aimed at facilitating the detection of landslides over wide areas. Specifically, the procedure evaluates changes of radar backscattered signals associated with land cover modifications that may be also caused by mass movements. After a one-time calibration of some parameters, the processing chain is able to automatically execute the download and preprocessing of images, the detection of SAR amplitude changes, and the identification of areas potentially affected by landslides, which are then displayed in a georeferenced map. This map should help decision makers and emergency managers to organize field investigations. The process of automatization is implemented with specific scripts running on a GNU/Linux operating system and exploiting modules of open-source software. We tested the processing chain, in back analysis, on an area of about 3000 km2 in central Papua New Guinea that was struck by a severe seismic sequence in February–March 2018. In the area, we simulated a periodic survey of about 7 months, from 12 November 2017 to 6 June 2018, downloading 36 Sentinel-1 images and performing 17 change detection analyses automatically. The procedure resulted in statistical and graphical evidence of widespread land cover changes that occurred just after the most severe seismic events. Most of the detected changes can be interpreted as mass movements triggered by the seismic shaking.

Description: The development of soil organic C (SOC) models capable to produce accurate predictions of the long term decomposition of exogenous organic matter (EOM) in soils is important for an effective management of organic amendments. However, reliable C modelling in amended soils requires specific optimization of current C models to take into account the high variability of EOM origin and properties. The aim of this work was to improve the prediction of C mineralization rates in amended soils by modifying the RothC model to encompass a better description of EOM quality. The standard RothC model, involving C input to the soil only as decomposable (DPM) or resistant (RPM) organic material, was modified by introducing additional pools of decomposable (DEOM), resistant (REOM) and humified (HEOM) EOM. The partitioning factors and decomposition rates of the additional EOM pools were estimated by model fitting to respiratory curves of amended soils. For this task, 30 EOMs from 8 contrasting groups (compost, anaerobic digestates, sewage sludges, agro-industrial wastes, crop residues, bioenergy by-products, animal residues, meat and bone meals), were added to 10 soils and incubated under different conditions. The modified Roth C model was fitted to C mineralization curves in amended soils with great accuracy (mean correlation coefficient: 0.995). Differently to the standard model, the EOM-optimized RothC was able to better accommodate the large variability in EOM source and composition, as indicated by the decrease in the root mean squared error of the simulations for different EOMs (from 29.9 % to 3.7 % and from 20.0 % to 2.5 % for bioethanol residue and household waste compost amended soils, respectively). Average decomposition rates for DEOM and REOM pools were 89 y−1 and 0.4 y−1, higher than the standard model coefficients for DPM (10 y−1) and RPM (0.3 y−1). Results indicate that explicit treatment of EOM heterogeneity enhances the model ability to describe amendment decomposition under laboratory conditions and provides useful information to improve C modelling on the effects of different EOM on C dynamics in agricultural soils. Future researches involve the validation of the modified model with field data and its application to long term simulation of SOC patterns in amended soil at regional scale under climate change.

Description: The development of soil organic C (SOC) models capable of producing accurate predictions for the long-term decomposition of exogenous organic matter (EOM) in soils is important for the effective management of organic amendments. However, reliable C modeling in amended soils requires specific optimization of current C models to take into account the high variability in EOM origin and properties. The aim of this work was to improve the prediction of C mineralization rates in amended soils by modifying the RothC model to encompass a better description of EOM quality. The standard RothC model, involving C input to the soil only as decomposable (DPM) or resistant (RPM) organic material, was modified by introducing additional pools of decomposable (DEOM), resistant (REOM) and humified (HEOM) EOM. The partitioning factors and decomposition rates of the additional EOM pools were estimated by model fitting to the respiratory curves of amended soils. For this task, 30 EOMs from 8 contrasting groups (compost, anaerobic digestates, sewage sludge, agro-industrial waste, crop residues, bioenergy by-products, animal residues and meat and bone meals) were added to 10 soils and incubated under different conditions. The modified RothC model was fitted to C mineralization curves in amended soils with great accuracy (mean correlation coefficient 0.995). In contrast to the standard model, the EOM-optimized RothC was able to better accommodate the large variability in EOM source and composition, as indicated by the decrease in the root mean square error of the simulations for different EOMs (from 29.9 to 3.7 % and 20.0 to 2.5 % for soils amended with bioethanol residue and household waste compost, respectively). The average decomposition rates for DEOM and REOM pools were 89 and 0.4 yr−1, higher than the standard model coefficients for DPM (10 yr−1) and RPM (0.3 yr−1). The results indicate that the explicit treatment of EOM heterogeneity enhances the model ability to describe amendment decomposition under laboratory conditions and provides useful information to improve C modeling on the effects of different EOM on C dynamics in agricultural soils. Future research will involve the validation of the modified model with field data and its application in the long-term simulation of SOC patterns in amended soil at regional scales under climate change.

Description: We tested a high-quality laser rangefinder binocular coupled with a GPS receiver connected to a Tablet PC running dedicated software to help recognize and map in the field recent rainfall-induced landslides. The system was tested in the period between March and April 2010, in the Monte Castello di Vibio area, Umbria, Central Italy. To test the equipment, we measured thirteen slope failures that were mapped previously during a visual reconnaissance field campaign conducted in February and March 2010. For reference, four slope failures were also mapped by walking the GPS receiver along the landslide perimeter. Comparison of the different mappings revealed that the geographical information obtained remotely for each landslide by the rangefinder binocular and GPS was comparable to the information obtained by walking the GPS around the landslide perimeter, and was superior to the information obtained through the visual reconnaissance mapping. Although our tests were not exhaustive, we maintain that the system is effective to map recent rainfall induced landslides in the field, and we foresee the possibility of using the same (or similar) system to map landslides, and other geomorphological features, in other areas.

Description: Event landslide inventory maps document the extent of populations of landslides caused by a single natural trigger, such as an earthquake, an intense rainfall event, or a rapid snowmelt event. Event inventory maps are important for landslide susceptibility and hazard modelling, and prove useful to manage residual risk after a landslide-triggering event. Standards for the preparation of event landslide inventory maps are lacking. Traditional methods are based on the visual interpretation of stereoscopic aerial photography, aided by field surveys. New and emerging techniques exploit remotely sensing data and semi-automatic algorithms. We describe the production of two event inventories prepared for the Pogliaschina catchment, Liguria, NW Italy. The two inventories show landslides triggered by an intense rainfall event on 25 October 2011, and were prepared through the visual interpretation of digital aerial photographs taken three days and thirty-three days after the event, and processing a very high resolution image taken by the WorldView II satellite four days after the event. We compare the two inventories qualitatively and quantitatively, using established and new metrics, and we discuss reasons for the differences and the similarities between the landslide maps. We expect that the results of our work can help deciding on the most appropriate method to prepare reliable event inventory maps, and to outline advantages and the limitations of the different methods.

Description: Event landslide inventory maps document the extent of populations of landslides caused by a single natural trigger, such as an earthquake, an intense rainfall event, or a rapid snowmelt event. Event inventory maps are important for landslide susceptibility and hazard modelling, and prove useful to manage residual risk after a landslide-triggering event. Standards for the preparation of event landslide inventory maps are lacking. Traditional methods are based on the visual interpretation of stereoscopic aerial photography, aided by field surveys. New and emerging techniques exploit remotely sensed data and semi-automatic algorithms. We describe the production and comparison of two independent event inventories prepared for the Pogliaschina catchment, Liguria, Northwest Italy. The two inventories show landslides triggered by an intense rainfall event on 25 October 2011, and were prepared through the visual interpretation of digital aerial photographs taken 3 days and 33 days after the event, and by processing a very-high-resolution image taken by the WorldView-2 satellite 4 days after the event. We compare the two inventories qualitatively and quantitatively using established and new metrics, and we discuss reasons for the differences between the two landslide maps. We expect that the results of our work can help in deciding on the most appropriate method to prepare reliable event inventory maps, and outline the advantages and the limitations of the different approaches.