Controls on the distribution of landslides triggered by the 2008 Wenchuan earthquake, Sichuan Province, China

Landsliding is the dominant mass wasting process in upland areas where the rate of river incision is higher than that of rock weathering of hillslopes. Although progressive erosional processes can provide sufficient conditions for slope failure, the majority of landslides are induced by earthquakes, rainstorms or a combination of these two. Landslides are also one of the most destructive geological processes, being the primary cause of damage and fatalities associated with severe storms and earthquakes in mountainous regions. On 12th May 2008 the magnitude 7.9 Wenchuan earthquake occurred in the Longmen Shan mountain range, on the northwest margin of the Sichuan Basin. Landsliding contributed greatly to the high death toll of over 70,000 and widespread infrastructural damage produced by the earthquake. The event offers an opportunity to both broaden the global database of seismically induced landslides and study the processes involved in earthquake-triggered landsliding, for a large continental thrust event with complex faulting mechanisms and diverse geophysical conditions. To achieve this, the following investigation builds upon recent advances in landslide remote sensing, to develop automated detection algorithms through which landslides can be accurately mapped using a range of satellite data. Using these techniques, a first order, regional landslide inventory map of slope failures triggered by the Wenchuan earthquake is produced, over an area of 12,000km2 along the main rupture zone. The production of this dataset demonstrates the application of automated classification techniques for the rapid generation of landslide data, for both geomorphological research and hazard management applications. The data is used to examine the interaction of fault rupture dynamics, topography and geology on landslide failure location, and identify key characteristics of the landslide distribution. Findings of the study demonstrate high levels of landslide occurrence along the entire mapped length of the rupture zone, and an exponential decay in landslide density with distance from the co-seismic surface ruptures. This is superimposed over a marked hanging wall effect, along with clear geological and topographic controls on landslide occurrence. Through generalised linear modelling, peak ground acceleration attenuation patterns, hillslope gradient, relief, local elevation and geology are identified as core controls on the location of landslides. The results of this research shed light on some increasingly recognised though poorly understood characteristics of seismically induced landslide distributions. The dataset produced contributes to the limited global database of earthquake-triggered landslide inventories, as well producing a widely applicable resource for further study of the Wenchuan earthquake and post-seismic landscape evolution

Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information

The complex temporal heterogeneity of rainfall coupled with mountainous physiographic context makes a great challenge in the development of accurate short-term rainfall forecasts. This study aims to explore the effectiveness of multiple rainfall sources (gauge measurement, and radar and satellite products) for assimilation-based multi-sensor precipitation estimates and make multi-step-ahead rainfall forecasts based on the assimilated precipitation. Bias correction procedures for both radar and satellite precipitation products were first built, and the radar and satellite precipitation products were generated through the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), respectively. Next, the synthesized assimilated precipitation was obtained by merging three precipitation sources (gauges, radars and satellites) according to their individual weighting factors optimized by nonlinear search methods. Finally, the multi-step-ahead rainfall forecasting was carried out by using the adaptive network-based fuzzy inference system (ANFIS). The Shihmen Reservoir watershed in northern Taiwan was the study area, where 641 hourly data sets of thirteen historical typhoon events were collected. Results revealed that the bias adjustments in QPESUMS and PERSIANN-CCS products did improve the accuracy of these precipitation products (in particular, 30-60% improvement rates for the QPESUMS, in terms of RMSE), and the adjusted PERSIANN-CCS and QPESUMS individually provided about 10% and 24% contribution accordingly to the assimilated precipitation. As far as rainfall forecasting is concerned, the results demonstrated that the ANFIS fed with the assimilated precipitation provided reliable and stable forecasts with the correlation coefficients higher than 0.85 and 0.72 for one- and two-hour-ahead rainfall forecasting, respectively. The obtained forecasting results are very valuable information for the flood warning in the study watershed during typhoon periods. © 2013 Elsevier B.V

Landslides

Landslides - Investigation and Monitoring offers a comprehensive overview of recent developments in the field of mass movements and landslide hazards. Chapter authors use in situ measurements, modeling, and remotely sensed data and methods to study landslides. This book provides a thorough overview of the latest efforts by international researchers on landslides and opens new possible research directions for further novel developments

Environmental monitoring: landslide assessment and risk management (Test site: Vernazza, Cinque Terre Natural Park)

Natural disasters, whether of meteorological origin such as cyclones, floods, tornadoes and droughts or having geological nature such as earthquakes, volcanoes and landslide, are well known for their devastating impacts on human life, economy and environment. Over recent decades, the people and the societies are becoming more vulnerable; although the frequency of natural events may be constant, human activities contribute to their increased intensity. Indeed, every year millions of people are affected by natural disasters globally and, only in the last decade, more than 80% of all disaster-related deaths were caused by natural hazards. The PhD work is part of the activities for the support and development of methodologies useful to improve the management of environmental emergencies. In particular, it focused on the analysis of environmental monitoring and disaster risk management, a systematic approach to identify, to assess and to reduce the potential risks produced by a disaster. This method (Disaster Risk Management) aims to reduce socio-economic vulnerabilities and deals with natural and man-made events. In the PhD thesis, in particular, the slope movements have been evaluated. Slope failures are generally not so costly as earthquakes or major floods, but they are more widespread, and over the years may cause more property loss than any other geological hazard. In many developing regions slope failures constitute a continuing and serious impact on the social and economic structure. Specifically, the Italian territory has always been subject to instability phenomena, because of the geological and morphological characteristic and because of "extreme" weather events that are repeated more frequently than in the past, in relation to climate change. Currently these disasters lead to the largest number of victims and damages to settlements, infrastructure and historical and cultural environmental, after the earthquakes. The urban development, especially in recent decades, resulted in an increase of the assets at risk and unstable areas, often due to constant human intervention badly designed that led to instability also places previously considered "safe". Prevention is therefore essential to minimize the damages caused by landslides The objectives of the conducted research were to investigate the different techniques and to check their potentiality, in order to evaluate the most appropriate instrument for landslide hazard assessment in terms of better compromise between time to perform the analysis and expected results. The attempt is to evaluate which are the best methodologies to use according to the scenario, taking into consideration both reachable accuracies and time constraints. Careful considerations will be performed on strengths, weaknesses and limitations inherent to each methodology. The characteristics associated with geographic, or geospatial, information technologies facilitate the integration of scientific, social and economic data, opening up interesting possibilities for monitoring, assessment and change detection activities, thus enabling better informed interventions in human and natural systems. This is an important factor for the success of emergency operations and for developing valuable natural disaster preparedness, mitigation and prevention systems. The test site was the municipality of Vernazza, which in October 2011 was subject to a extreme rainfall which led to the occurrence of a series of landslides along the Vernazzola stream, which have emphasized the flood event that affected the water cours

Remote Sensing Image Scene Classification: Benchmark and State of the Art

Remote sensing image scene classification plays an important role in a wide range of applications and hence has been receiving remarkable attention. During the past years, significant efforts have been made to develop various datasets or present a variety of approaches for scene classification from remote sensing images. However, a systematic review of the literature concerning datasets and methods for scene classification is still lacking. In addition, almost all existing datasets have a number of limitations, including the small scale of scene classes and the image numbers, the lack of image variations and diversity, and the saturation of accuracy. These limitations severely limit the development of new approaches especially deep learning-based methods. This paper first provides a comprehensive review of the recent progress. Then, we propose a large-scale dataset, termed "NWPU-RESISC45", which is a publicly available benchmark for REmote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This dataset contains 31,500 images, covering 45 scene classes with 700 images in each class. The proposed NWPU-RESISC45 (i) is large-scale on the scene classes and the total image number, (ii) holds big variations in translation, spatial resolution, viewpoint, object pose, illumination, background, and occlusion, and (iii) has high within-class diversity and between-class similarity. The creation of this dataset will enable the community to develop and evaluate various data-driven algorithms. Finally, several representative methods are evaluated using the proposed dataset and the results are reported as a useful baseline for future research.Comment: This manuscript is the accepted version for Proceedings of the IEE

Environmental monitoring: landslide assessment and risk management (Test site: Vernazza, Cinque Terre Natural Park)

Natural disasters, whether of meteorological origin such as cyclones, floods, tornadoes and droughts or having geological nature such as earthquakes, volcanoes and landslide, are well known for their devastating impacts on human life, economy and environment. Over recent decades, the people and the societies are becoming more vulnerable; although the frequency of natural events may be constant, human activities contribute to their increased intensity. Indeed, every year millions of people are affected by natural disasters globally and, only in the last decade, more than 80% of all disaster-related deaths were caused by natural hazards. The PhD work is part of the activities for the support and development of methodologies useful to improve the management of environmental emergencies. In particular, it focused on the analysis of environmental monitoring and disaster risk management, a systematic approach to identify, to assess and to reduce the potential risks produced by a disaster. This method (Disaster Risk Management) aims to reduce socio-economic vulnerabilities and deals with natural and man-made events. In the PhD thesis, in particular, the slope movements have been evaluated. Slope failures are generally not so costly as earthquakes or major floods, but they are more widespread, and over the years may cause more property loss than any other geological hazard. In many developing regions slope failures constitute a continuing and serious impact on the social and economic structure. Specifically, the Italian territory has always been subject to instability phenomena, because of the geological and morphological characteristic and because of "extreme" weather events that are repeated more frequently than in the past, in relation to climate change. Currently these disasters lead to the largest number of victims and damages to settlements, infrastructure and historical and cultural environmental, after the earthquakes. The urban development, especially in recent decades, resulted in an increase of the assets at risk and unstable areas, often due to constant human intervention badly designed that led to instability also places previously considered "safe". Prevention is therefore essential to minimize the damages caused by landslides The objectives of the conducted research were to investigate the different techniques and to check their potentiality, in order to evaluate the most appropriate instrument for landslide hazard assessment in terms of better compromise between time to perform the analysis and expected results. The attempt is to evaluate which are the best methodologies to use according to the scenario, taking into consideration both reachable accuracies and time constraints. Careful considerations will be performed on strengths, weaknesses and limitations inherent to each methodology. The characteristics associated with geographic, or geospatial, information technologies facilitate the integration of scientific, social and economic data, opening up interesting possibilities for monitoring, assessment and change detection activities, thus enabling better informed interventions in human and natural systems. This is an important factor for the success of emergency operations and for developing valuable natural disaster preparedness, mitigation and prevention systems. The test site was the municipality of Vernazza, which in October 2011 was subject to a extreme rainfall which led to the occurrence of a series of landslides along the Vernazzola stream, which have emphasized the flood event that affected the water course