Laboratory Assessment of an In-Place Inclinometer Chain for Structural and Geotechnical Monitoring

The necessity of early warning systems to ensure people's safety requires the usage of real-time monitoring instrumentation. To meet the required real-time monitoring performance, in-place inclinometer systems represent one of the most common solutions to obtain accurate measures over time. This paper presents the results of a laboratory tests campaign performed on the prototypes and preproduction samples of an in-place inclinometer chain for structural and geotechnical monitoring applications. First, each element sensor has been calibrated to reach a proper level of measure accuracy. Eventually, laboratory tests are carried out on both a single instrument (element) and on the complete measurement chain (system). The adopted centering device, obtained as a combination of a Cardan joint and four spring plungers avoids bending of elements by preventing fictitious displacement measurements and permits the creation of a kinematic chain that accommodates the displacements of a grooveless tube. A specially designed and constructed test set-up that permits assigning a movement to each node has been employed to test a specifically designed centering device and check the system stability over time. Different scenarios have been investigated to determine the accuracy and repeatability of the measures in replicating real cases. The results demonstrated the necessity of validating a measurement chain by analyzing its overall behavior and not limiting the study on the performances of a single element.

Application of Bagging, Boosting and Stacking Ensemble and EasyEnsemble Methods for Landslide Susceptibility Mapping in the Three Gorges Reservoir Area of China

Since the impoundment of the Three Gorges Reservoir area in 2003, the potential risks of geological disasters in the reservoir area have increased significantly, among which the hidden dangers of landslides are particularly prominent. To reduce casualties and damage, efficient and precise landslide susceptibility evaluation methods are important. Multiple ensemble models have been used to evaluate the susceptibility of the upper part of Badong County to landslides. In this study, EasyEnsemble technology was used to solve the imbalance between landslide and nonlandslide sample data. The extracted evaluation factors were input into three bagging, boosting, and stacking ensemble models for training, and landslide susceptibility mapping (LSM) was drawn. According to the importance analysis, the important factors affecting the occurrence of landslides are altitude, terrain surface texture (TST), distance to residences, distance to rivers and land use. The influences of different grid sizes on the susceptibility results were compared, and a larger grid was found to lead to the overfitting of the prediction results. Therefore, a 30 m grid was selected as the evaluation unit. The accuracy, area under the curve (AUC), recall rate, test set precision, and kappa coefficient of a multi-grained cascade forest (gcForest) model with the stacking method were 0.958, 0.991, 0.965, 0.946, and 0.91, respectively, which a significantly better than the values produced by the other models.

The Impact of Earthquakes on Public Health: A Narrative Review of Infectious Diseases in the Post-Disaster Period Aiming to Disaster Risk Reduction

Earthquakes are among the most impressive natural phenomena with very high potential to set off a chain of effects that significantly affects public health through casualties and injuries. Related disasters are attributed not only to the strong ground motion and coseismic phenomena but also to secondary effects, comprising mainly landslides and tsunamis, among others. All these can create harsh conditions favorable for the emergence of infectious diseases that are capable of causing additional human and economic losses and disruption of the emergency and recovery process. The present study comprises an extensive narrative review of the existing literature on the earthquake-triggered infectious diseases recorded worldwide, along with their symptoms, causative pathogens, associated risk factors, most vulnerable population groups, and prevention strategies. Respiratory, gastrointestinal, and vector-borne diseases, as well as wound and skin infections, are mainly recorded among the earthquake-affected population. Measures for effectively preventing earthquake-triggered infectious diseases are also proposed. One of the widely proposed measures is the establishment of a proper disease surveillance system in order to immediately and effectively identify the pre- and post-disaster occurrence of infectious diseases. This approach significantly contributes to disease trends monitoring, validation of early warning, and support of the emergency response and recovery actions.

The Use of Soil Moisture and Pore-Water Pressure Sensors for the Interpretation of Landslide Behavior in Small-Scale Physical Models

This paper presents some of the results and experiences in monitoring the hydraulic response of downscaled slope models under simulated rainfall in 1 g. The downscaled slope model platform was developed as part of a four-year research project, "Physical modeling of landslide remediation constructions' behavior under static and seismic actions", and its main components are briefly described with the particular focus on the sensor network that allows monitoring changes in soil moisture and pore-water pressure (pwp). The technical characteristics of the sensors and the measurement methods used to provide the metrics are described in detail. Some data on the hydraulic and mechanical responses obtained from the conducted tests on slope models built from different soil types under different test conditions are presented and interpreted in the context of rainfall-induced landslides. The results show that the sensor network used is suitable for monitoring changes in the soil moisture and pwp in the model, both in terms of the transient rainfall infiltration through partially saturated soil and in terms of the rise in the water table and pwp build-up under fully saturated conditions. It is shown how simultaneous monitoring of soil moisture and pwp can be used to reconstruct stress paths that the monitored points undergo during different test phases. Finally, some peculiarities related to hydraulic hysteresis and surface erosion that were observed in some of tests are discussed, as well as possible difficulties in achieving and maintaining the targeted initial moisture distribution in slope models.

Identification of Landslides in Mountainous Area with the Combination of SBAS-InSAR and Yolo Model

Landslides have been frequently occurring in the high mountainous areas in China and poses serious threats to peoples' lives and property, economic development, and national security. Detecting and monitoring quiescent or active landslides is important for predicting risks and mitigating losses. However, traditional ground survey methods, such as field investigation, GNSS, and total stations, are only suitable for field investigation at a specific site rather than identifying landslides over a large area, as they are expensive, time-consuming, and laborious. In this study, the feasibility of using SBAS-InSAR to detect landslides in the high mountainous areas along the Yunnan Myanmar border was tested first, with fifty-four IW mode Sentinel-1A ascending scenes from 12 January 2019 to 8 December 2020. Next, the Yolo deep-learning model with Gaofen-2 images captured on 5 December 2020 was tested. Finally, the two techniques were combined to achieve better performance, given each of them has intrinsic limitations on landslide detection. The experiment indicated that the combination could improve the match rate between detection results and references, which implied that the performance of landslide detection can be improved with the fusion of time series SAR images and optical images.

Effect of disaster training on knowledge regarding flood risk management amongst families with older people

The elderly population is of utmost importance amongst vulnerable populations during disasters because they experience reduced functional abilities, cognitive disturbance, dementia, weak physical conditions and various degenerative diseases. This study aimed to assess the effect of disaster preparedness training on knowledge regarding flood disaster preparedness and management in families with older people. This study was conducted using a quasi-experimental pre-post design with 30 participants in natural hazard preparedness training using purposive sampling. The results of this study showed a significant change in general knowledge on disaster and flood (12.9 and 20 points, respectively). Disaster preparedness practice was good, as reflected in actions performed before, during and after disaster. Before a flood occurs, families prepare a disaster preparedness bag for important documents as well as logistics (e.g. food) for emergencies and equipment for evacuation whilst also storing valuable goods in a safe place. During floods, families prioritise older people's evacuation whilst seeking information about the flood through neighbours, walkie-talkies, handphones, television and radio as well as ensuring that the necessary logistics are taken care of. After the disaster, families clean their properties, provide clean water and toilet facilities for the family, check the health of family members that may be impacted by the flood and make sure that all electrical panels are safe. It is concluded that disaster training affects the knowledge of flood management in families with older people.

Landslide Sensitivity and Response to Precipitation Changes in Wet and Dry Climates

Slow-moving landslides are hydrologically driven. Yet, landslide sensitivity to precipitation, and in particular, precipitation extremes, is difficult to constrain because landslides occur under diverse hydroclimatological conditions. Here we use standardized open-access satellite radar interferometry data to quantify the sensitivity of 38 landslides to both a record drought and extreme rainfall that occurred in California between 2015 and 2020. These landslides are hosted in similar rock types, but span more than ∼2 m/yr in mean annual rainfall. Despite the large differences in hydroclimate, we found these landslides exhibited surprisingly similar behaviors and hydrologic sensitivity, which was characterized by faster (slower) than average velocities during wetter (drier) than average years, once the impact of the drought diminished. Our findings may be representative of future landslide behaviors in California where precipitation extremes are predicted to become more frequent with climate change.

Landslide Susceptibility Mapping Using Machine Learning Algorithm Validated by Persistent Scatterer In-SAR Technique

Landslides are the most catastrophic geological hazard in hilly areas. The present work intends to identify landslide susceptibility along Karakorum Highway (KKH) in Northern Pakistan, using landslide susceptibility mapping (LSM). To compare and predict the connection between causative factors and landslides, the random forest (RF), extreme gradient boosting (XGBoost), k nearest neighbor (KNN) and naive Bayes (NB) models were used in this research. Interferometric synthetic aperture radar persistent scatterer interferometry (PS-InSAR) technology was used to explore the displacement movement of retrieved models. Initially, 332 landslide areas alongside the Karakorum Highway were found to generate the landslide inventory map using various data. The landslides were categorized into two sections for validation and training, of 30% and 70%. For susceptibility mapping, thirteen landslide-condition factors were created. The area under curve (AUC) of the receiver operating characteristic (ROC) curve technique was utilized for accuracy comparison, yielding 83.08, 82.15, 80.31, and 72.92% accuracy for RF, XGBoost, KNN, and NB, respectively. The PS-InSAR technique demonstrated a high deformation velocity along the line of sight (LOS) in model-sensitive areas. The PS-InSAR technique was used to evaluate the slope deformation velocity, which can be used to improve the LSM for the research region. The RF technique yielded superior findings, integrating with the PS-InSAR outcomes to provide the region with a new landslide susceptibility map. The enhanced model will help mitigate landslide catastrophes, and the outcomes may help ensure the roadway’s safe functioning in the study region.

Malnutrition and Associated Risk Factors among Children 6-59 Months Old in the Landslide-Prone Bududa District, Eastern Uganda: A Cohort Study

BACKGROUND

The United Nations Sustainable Development Goal 2.2 calls for an end to all forms of malnutrition. This might be derailed due to persistent landslide disasters in low-income countries like Uganda.

OBJECTIVES

The prevalence of malnutrition and the impact of seasonal variations and associated factors were assessed among children aged 6-59 mo in the landslide-affected households in Bududa District, eastern Uganda.

METHODS

A prospective cohort study using a 2-stage simple random technique was applied to select 422 households including 392 children during May-August (food-plenty season) 2019. After 6 mo, in January-March (food-poor season) 2020, 388 households and 366 children were assessed. Socioeconomic and demographic data were collected using structured questionnaires. Child malnutrition outcomes were defined according to WHO criteria. Factors associated with malnutrition outcomes were identified by bivariate and multivariate logistic regression.

RESULTS

Stunting, underweight, wasting, and overweight prevalences were 37.7%, 13.3%, 3.6%, and 4.3%, respectively, in the food-plenty season and 42.6%, 14.2%, 2.1%, and 2.7%, respectively, in the food-poor season. Residing in the landslide-affected sub-county increased the odds for stunting [adjusted OR (aOR): 1.68; 95% CI: 1.08, 2.59; P = 0.025] and underweight (aOR = 4.25; 95% CI: 1.10, 15.36; P = 0.032) for children in the food-plenty season. Child age, sex, breastfeeding status, a nonimproved drinking water source, migration of any household member, and parents' education were significant risk factors in the food-plenty season. In the food-poor season, parents' education status, loss of any household member, child sex, and child age were significant risk factors.

CONCLUSIONS

Stunting and underweight were more prevalent in the food-poor season while wasting and overweight were more prevalent in the food-plenty season. With the exception of child age, child sex, and parents' education, child malnutrition risk factors differed between food-plenty and food-poor seasons. There is a need to address seasonality factors in program interventions targeting children <5 y in landslide-prone areas.

Strategic protection of landslide vulnerable mountains for biodiversity conservation under land-cover and climate change impacts

Landslides are triggered more often by human-induced changes, such as deforestation, infrastructure building, and increasing precipitation extremes, because of climate change. The huge economic and societal loss calls for a more cost-effective way to reduce risks and ensure sustainable development. Land-cover and land-use changes not only increase landslide susceptibility but also drive habitat loss and species extinctions. The high spatial overlap between landslide susceptibility and biodiversity in mountains provides an opportunity to achieve co-benefits in conservation and development. The identification of vulnerable mountains with both high biodiversity and landslide susceptibility prioritizes the regions for expansion of protected areas, forest conservation, and restoration, providing a nature-based solution to mitigate landslide risks through the protection of natural habitat.

Natural disasters impose huge uncertainty and loss to human lives and economic activities. Landslides are one disaster that has become more prevalent because of anthropogenic disturbances, such as land-cover changes, land degradation, and expansion of infrastructure. These are further exacerbated by more extreme precipitation due to climate change, which is predicted to trigger more landslides and threaten sustainable development in vulnerable regions. Although biodiversity conservation and development are often regarded as having a trade-off relationship, here we present a global analysis of the area with co-benefits, where conservation through expanding protection and reducing deforestation can not only benefit biodiversity but also reduce landslide risks to human society. High overlap exists between landslide susceptibility and areas of endemism for mammals, birds, and amphibians, which are mostly concentrated in mountain regions. We identified 247 mountain ranges as areas with high vulnerability, having both exceptional biodiversity and landslide risks, accounting for 25.8% of the global mountainous areas. Another 31 biodiverse mountains are classified as future vulnerable mountains as they face increasing landslide risks because of predicted climate change and deforestation. None of these 278 mountains reach the Aichi Target 11 of 17% coverage by protected areas. Of the 278 mountains, 52 need immediate actions because of high vulnerability, severe threats from future deforestation and precipitation extremes, low protection, and high-population density and anthropogenic activities. These actions include protected area expansion, forest conservation, and restoration where it could be a cost-effective way to reduce the risks of landslides.

Floods, landslides and COVID-19 in the Uttarakhand State, India: Impact of Ongoing Crises on Public Health

The Uttarakhand State, known for its Himalayan Mountains, is a territory in Northern India that is extremely vulnerable to earthquakes, landslides, and floods. Currently, due to the COVID-19 outbreak, India is facing the dual challenge of containing a pandemic and responding to natural disasters. This situation can have a negative impact on the health and the economic development of the region, leading to a long-lasting humanitarian crisis that can disrupt even more, the already overburdened health service. In addition, it can pose serious threats to the wellbeing of the population as it complicates physical distancing and other COVID-19 prevention measures. It is of utmost importance to analyse the impact of floods, landslides, and COVID-19 pandemic on the health system of the Uttarakhand State, and how these crises interact with each other.

Statistical Time-Series Analysis of Interferometric Coherence from Sentinel-1 Sensors for Landslide Detection and Early Warning

Landslides are one of the most destructive natural hazards worldwide, affecting greatly built-up areas and critical infrastructure, causing loss of human lives, injuries, destruction of properties, and disturbance in everyday commute. Traditionally, landslides are monitored through time consuming and costly in situ geotechnical investigations and a wide range of conventional means, such as inclinometers and boreholes. Earth Observation and the exploitation of the freely available Copernicus datasets, and especially Sentinel-1 Synthetic Aperture Radar (SAR) images, can assist in the systematic monitoring of landslides, irrespective of weather conditions and time of day, overcoming the restrictions arising from in situ measurements. In the present study, a comprehensive statistical analysis of coherence obtained through processing of a time-series of Sentinel-1 SAR imagery was carried out to investigate and detect early indications of a landslide that took place in Cyprus on 15 February 2019. The application of the proposed methodology led to the detection of a sudden coherence loss prior to the landslide occurrence that can be used as input to Early Warning Systems, giving valuable on-time information about an upcoming landslide to emergency response authorities and the public, saving numerous lives. The statistical significance of the results was tested using Analysis of Variance (ANOVA) tests and two-tailed t-tests.

Worldwide Research Trends in Landslide Science

Landslides are generated by natural causes and by human action, causing various geomorphological changes as well as physical and socioeconomic loss of the environment and human life. The study, characterization and implementation of techniques are essential to reduce land vulnerability, different socioeconomic sector susceptibility and actions to guarantee better slope stability with a significant positive impact on society. The aim of this work is the bibliometric analysis of the different types of landslides that the United States Geological Survey (USGS) emphasizes, through the SCOPUS database and the VOSviewer software version 1.6.17, for the analysis of their structure, scientific production, and the close relationship with several scientific fields and its trends. The methodology focuses on: (i) search criteria; (ii) data extraction and cleaning; (iii) generation of graphs and bibliometric mapping; and (iv) analysis of results and possible trends. The study and analysis of landslides are in a period of exponential growth, focusing mainly on techniques and solutions for the stabilization, prevention, and categorization of the most susceptible hillslope sectors. Therefore, this research field has the full collaboration of various authors and places a significant focus on the conceptual evolution of the landslide science.

Food insecurity and compound environmental shocks in Nepal: Implications for a changing climate

Food insecurity is a key global health challenge that is likely to be exacerbated by climate change. Though climate change is associated with an increased frequency of extreme weather events, little is known about how multiple environmental shocks in close succession interact to impact household health and well-being. In this paper, we assess how earthquake exposure followed by monsoon rainfall anomalies affect food insecurity in Nepal. We link food security data from the 2016 Nepal Demographic and Health Survey to data on shaking intensity during the 2015 Gorkha earthquake and rainfall anomalies during the 2015 monsoon season. We then exploit spatial variation in exposure to the earthquake and monsoon rainfall anomalies to isolate their independent and compound effects. We find that earthquake exposure alone was not associated with an increased likelihood of food insecurity, likely due in part to effective food aid distribution. However, the effects of rainfall anomalies differed by severity of earthquake exposure. Among households minimally impacted by the earthquake, low rainfall was associated with increased food insecurity, likely due to lower agricultural productivity in drought conditions. Among households that experienced at least moderate shaking, greater rainfall was positively associated with food insecurity, particularly in steep, mountainous areas. In these locations, rainfall events disproportionately increased landslides, which damaged roads, disrupted distribution of food aid, and destroyed agricultural land and assets. Additional research on the social impacts of compound environmental shocks is needed to inform adaptation strategies that work to improve well-being in the face of climate change.

Extended Residual-State Creep Test and Its Application for Landslide Stability Assessment

This paper contains the results of a newly developed residual-state creep test performed to determine the behavior of a selected geomaterial in the context of reactivated landslides. Soil and rock creep is a time-dependent phenomenon in which a deformation occurs under constant stress. Based on the examination results, it was found that the tested clayey material (from Kobe, Japan) shows tertiary creep behavior only under shear stress higher than the residual strength condition and primary and secondary creep behavior under shear stress lower or equal to the residual strength condition. Based on the data, a model for predicting the critical or failure time is introduced. The study traces the development of the limit state based on the contact model corresponding to Blair’s body. The time to occurrence of the conditions necessary for unlimited creep on the surface is estimated. As long-term precipitation and infiltrating water in the area of the landslides are identified as the key phenomena initiating collapse, the work focuses on the prediction of landslides with identified surfaces of potential damage as a result of changes in the saturation state. The procedure outlined is applied to a case study and considerations as to when the necessary safety work should be carried out are presented.

Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems

Worldwide, cities with mountainous areas struggle with an increasing landslide risk as a consequence of global warming and population growth, especially in low-income informal settlements. Landslide Early Warning Systems (LEWS) are an effective measure to quickly reduce these risks until long-term risk mitigation measures can be realized. To date however, LEWS have only rarely been implemented in informal settlements due to their high costs and complex operation. Based on modern Internet of Things (IoT) technologies such as micro-electro-mechanical systems (MEMS) sensors and the LoRa (Long Range) communication protocol, the Inform@Risk research project is developing a cost-effective geosensor network specifically designed for use in a LEWS for informal settlements. It is currently being implemented in an informal settlement in the outskirts of Medellin, Colombia for the first time. The system, whose hardware and firmware is open source and can be replicated freely, consists of versatile LoRa sensor nodes which have a set of MEMS sensors (e.g., tilt sensor) on board and can be connected to various different sensors including a newly developed low cost subsurface sensor probe for the detection of ground movements and groundwater level measurements. Complemented with further innovative measurement systems such as the Continuous Shear Monitor (CSM) and a flexible data management and analysis system, the newly developed LEWS offers a good benefit-cost ratio and in the future can hopefully find application in other parts of the world.

Deformation Prediction of Unstable Slopes Based on Real-Time Monitoring and DeepAR Model

With increased urbanization, accidents related to slope instability are frequently encountered in construction sites. The deformation and failure mechanism of a landslide is a complex dynamic process, which seriously threatens people's lives and property. Currently, prediction and early warning of a landslide can be effectively performed by using Internet of Things (IoT) technology to monitor the landslide deformation in real time and an artificial intelligence algorithm to predict the deformation trend. However, if a slope failure occurs during the construction period, the builders and decision-makers find it challenging to effectively apply IoT technology to monitor the emergency and assist in proposing treatment measures. Moreover, for projects during operation (e.g., a motorway in a mountainous area), no recognized artificial intelligence algorithm exists that can forecast the deformation of steep slopes using the huge data obtained from monitoring devices. In this context, this paper introduces a real-time wireless monitoring system with multiple sensors for retrieving high-frequency overall data that can describe the deformation feature of steep slopes. The system was installed in the Qili connecting line of a motorway in Zhejiang Province, China, to provide a technical support for the design and implementation of safety solutions for the steep slopes. Most of the devices were retained to monitor the slopes even after construction. The machine learning Probabilistic Forecasting with Autoregressive Recurrent Networks (DeepAR) model based on time series and probabilistic forecasting was introduced into the project to predict the slope displacement. The predictive accuracy of the DeepAR model was verified by the mean absolute error, the root mean square error and the goodness of fit. This study demonstrates that the presented monitoring system and the introduced predictive model had good safety control ability during construction and good prediction accuracy during operation. The proposed approach will be helpful to assess the safety of excavated slopes before constructing new infrastructures.

Advances on Measuring Deep-Seated Ground Deformations Using Robotized Inclinometer System

In the field of geo-hazards and geo-engineering, monitoring networks represent a key element for the geological risk assessment and the design and management of large infrastructures construction. In the last decade, we have observed a strong development on remote sensing techniques but just small changes in the subsoil observations. However, this type of measurement is very important to have a three-dimensional representation of the studied area, since the surface measurements often represent a sum of deformations that develop in a complex way in the subsoil. In this paper, we present a robotic inclinometer system developed to acquire deep-seated ground deformations in boreholes. This instrumentation combines advantages offered by manual inclinometer measurements with a robotized approach that improves the results in term of accuracy, revisiting time, and site accessibility. The Automated Inclinometer System (AIS) allows one to explore automatically all the length of the monitored borehole using just one inclinometer probe with a semi-wireless system. The paper presents the system and a detailed dataset of measurements acquired on three inclinometer tubes installed for the monitoring of the construction phase of the new Line C Metro of Rome. The dataset was acquired in real monitored site and undisturbed conditions and can represent a benchmark for modern inclinometer measurements.

Spatial and temporal patterns and the socioeconomic impacts of landslides in the tropical and mountainous Colombian Andes

Landslides are a natural hazard that presents a major threat to human life and infrastructure. Although they are a very common phenomenon in Colombia, there is a lack of analysis that entails national and comprehensive spatial, temporal, and socioeconomic evaluations of such events based on historical records. This study provides a detailed assessment of the spatial and temporal patterns and the socioeconomic impacts associated with landslides that occurred in the country between 1900 and 2018. Two national landslide databases were consulted and this information was complemented by local and regional landslide catalogues. A total of 30,730 landslides were recorded in the 118-year period. Rainfall is the most common trigger of landslides, responsible for 92 per cent of those registered, but most fatalities (68 per cent) are due to landslides caused by volcanic activity and earthquakes. An 'fN curve' revealed a very high frequency of small and moderate fatal landslides in the time frame.

A Multi-Module Fixed Inclinometer for Continuous Monitoring of Landslides: Design, Development, and Laboratory Testing

Continuous monitoring of landslides is of basic importance for understanding their behavior, defining their 3D geometry, and providing a basis for early warning purposes. While a number of instrumentations can be used for tracking surface displacement, only automatic or fixed multi-module inclinometers can be used for continuous monitoring of displacement at depth, providing valuable information for landslide geometry reconstruction. Since these instruments are very expensive, thus rarely used, a low-cost and multi-module fixed inclinometer for continuous landslide monitoring has been developed. In this paper, the electronics of the system, including sensor characteristics and optimization, controlling software, and structure are presented. For system development, a single module prototype was first developed and tested in the field to ensure sufficient measuring performance. Subsequently, the multi-module system was designed, assembled, and tested in controlled conditions. Test results indicate the good performance of the system with a displacement measuring accuracy of 0.37% of the length of the inclinometer chain. The linearity test indicates the high linearity of the measures, especially in the range ±20°, which is the typical operating range of such kinds of instrumentations. The thermal efficiency test indicates the high efficiency of the system in preventing measuring errors caused by thermal drifting.

Investigation on Surface Tilting in the Failure Process of Shallow Landslides

In recent decades, early warning systems to predict the occurrence of landslides using tilt sensors have been developed and employed in slope monitoring due to their low cost and simple installation. Although many studies have been carried out to validate the efficiency of these early warning systems, few studies have been carried out to investigate the tilting direction of tilt sensors at the slope surface, which have revealed controversial results in field monitoring. In this paper, the tilting direction and the pre-failure tilting behavior of slopes were studied by performing a series of model tests as well as two field tests. These tests were conducted under various testing conditions. Tilt sensors with different rod lengths were employed to investigate the mechanism of surface tilting. The test results show that the surface tilting measured by the tilt sensors with no rods and those with short rods located above the slip surface are consistent, while the tilting monitored by the tilt sensors with long rods implies an opposite rotational direction. These results are important references to understand the controversial surface tilting behavior in in situ landslide monitoring cases and imply the correlation between the depth of the slip surface of the slope and the surface tilting in in situ landslide monitoring cases, which can be used as the standard for tilt sensor installation in field monitoring.

IoT-Based Geotechnical Monitoring of Unstable Slopes for Landslide Early Warning in the Darjeeling Himalayas

In hilly areas across the world, landslides have been an increasing menace, causing loss of lives and properties. The damages instigated by landslides in the recent past call for attention from authorities for disaster risk reduction measures. Development of an effective landslide early warning system (LEWS) is an important risk reduction approach by which the authorities and public in general can be presaged about future landslide events. The Indian Himalayas are among the most landslide-prone areas in the world, and attempts have been made to determine the rainfall thresholds for possible occurrence of landslides in the region. The established thresholds proved to be effective in predicting most of the landslide events and the major drawback observed is the increased number of false alarms. For an LEWS to be successfully operational, it is obligatory to reduce the number of false alarms using physical monitoring. Therefore, to improve the efficiency of the LEWS and to make the thresholds serviceable, the slopes are monitored using a sensor network. In this study, micro-electro-mechanical systems (MEMS)-based tilt sensors and volumetric water content sensors were used to monitor the active slopes in Chibo, in the Darjeeling Himalayas. The Internet of Things (IoT)-based network uses wireless modules for communication between individual sensors to the data logger and from the data logger to an internet database. The slopes are on the banks of mountain rivulets (jhoras) known as the sinking zones of Kalimpong. The locality is highly affected by surface displacements in the monsoon season due to incessant rains and improper drainage. Real-time field monitoring for the study area is being conducted for the first time to evaluate the applicability of tilt sensors in the region. The sensors are embedded within the soil to measure the tilting angles and moisture content at shallow depths. The slopes were monitored continuously during three monsoon seasons (2017-2019), and the data from the sensors were compared with the field observations and rainfall data for the evaluation. The relationship between change in tilt rate, volumetric water content, and rainfall are explored in the study, and the records prove the significance of considering long-term rainfall conditions rather than immediate rainfall events in developing rainfall thresholds for the region.

Landslides on Ceres: Diversity and Geologic Context

Landslides are among the most widespread geologic features on Ceres. Using data from Dawn's Framing Camera, landslides were previously classified based upon geomorphologic characteristics into one of three archetypal categories, Type 1(T1), Type 2 (T2), and Type 3 (T3). Due to their geologic context, variation in age, and physical characteristics, most landslides on Ceres are, however, intermediate in their morphology and physical properties between the archetypes of each landslide class. Here we describe the varied morphology of individual intermediate landslides, identify geologic controls that contribute to this variation, and provide first-order quantification of the physical properties of the continuum of Ceres's surface flows. These intermediate flows appear in varied settings and show a range of characteristics, including those found at contacts between craters, those having multiple trunks or lobes; showing characteristics of both T2 and T3 landslides; material slumping on crater rims; very small, ejecta-like flows; and those appearing inside of catenae. We suggest that while their morphologies can vary, the distribution and mechanical properties of intermediate landslides do not differ significantly from that of archetypal landslides, confirming a link between landslides and subsurface ice. We also find that most intermediate landslides are similar to Type 2 landslides and formed by shallow failure. Clusters of these features suggest ice enhancement near Juling, Kupalo and Urvara craters. Since the majority of Ceres's landslides fall in the intermediate landslide category, placing their attributes in context contributes to a better understanding of Ceres's shallow subsurface and the nature of ground ice.

A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions

Disaster robotics has become a research area in its own right, with several reported cases of successful robot deployment in actual disaster scenarios. Most of these disaster deployments use aerial, ground, or underwater robotic platforms. However, the research involving autonomous boats or Unmanned Surface Vehicles (USVs) for Disaster Management (DM) is currently spread across several publications, with varying degrees of depth, and focusing on more than one unmanned vehicle—usually under the umbrella of Unmanned Marine Vessels (UMV). Therefore, the current importance of USVs for the DM process in its different phases is not clear. This paper presents the first comprehensive survey about the applications and roles of USVs for DM, as far as we know. This work demonstrates that there are few current deployments in disaster scenarios, with most of the research in the area focusing on the technological aspects of USV hardware and software, such as Guidance Navigation and Control, and not focusing on their actual importance for DM. Finally, to guide future research, this paper also summarizes our own contributions, the lessons learned, guidelines, and research gaps.

Hybrid Integration Approach of Entropy with Logistic Regression and Support Vector Machine for Landslide Susceptibility Modeling

The main purpose of the present study is to apply three classification models, namely, the index of entropy (IOE) model, the logistic regression (LR) model, and the support vector machine (SVM) model by radial basis function (RBF), to produce landslide susceptibility maps for the Fugu County of Shaanxi Province, China. Firstly, landslide locations were extracted from field investigation and aerial photographs, and a total of 194 landslide polygons were transformed into points to produce a landslide inventory map. Secondly, the landslide points were randomly split into two groups (70/30) for training and validation purposes, respectively. Then, 10 landslide explanatory variables, such as slope aspect, slope angle, altitude, lithology, mean annual precipitation, distance to roads, distance to rivers, distance to faults, land use, and normalized difference vegetation index (NDVI), were selected and the potential multicollinearity problems between these factors were detected by the Pearson Correlation Coefficient (PCC), the variance inflation factor (VIF), and tolerance (TOL). Subsequently, the landslide susceptibility maps for the study region were obtained using the IOE model, the LR-IOE, and the SVM-IOE model. Finally, the performance of these three models was verified and compared using the receiver operating characteristics (ROC) curve. The success rate results showed that the LR-IOE model has the highest accuracy (90.11%), followed by the IOE model (87.43%) and the SVM-IOE model (86.53%). Similarly, the AUC values also showed that the prediction accuracy expresses a similar result, with the LR-IOE model having the highest accuracy (81.84%), followed by the IOE model (76.86%) and the SVM-IOE model (76.61%). Thus, the landslide susceptibility map (LSM) for the study region can provide an effective reference for the Fugu County government to properly address land planning and mitigate landslide risk.

Landslides and dam damage resulting from the Jiuzhaigou earthquake (8 August 2017), Sichuan, China

At 21.19 on 8 August 2017, an Ms 7.0 earthquake struck the Jiuzhaigou scenic spot in northwestern Sichuan Province, China. The Jiuzhaigou earthquake is a strike-slip earthquake with a focal depth of 20 km at 33.20° N and 103.82° E, and was caused by two concealed faults. According to emergency investigations and remote sensing interpretations, the Jiuzhaigou earthquake triggered 1780 landslides, damaged one dam (Nuorilang Waterfall) and broke one dam (Huohua Lake). The landslides mainly occurred in the Rize Valley and Shuzheng Valley and in Jiuzhai Paradise. The landslides involved hanging wall and back-slope effects, and the slope angle, slope aspect, seismic faults and valley trend were obviously related to the occurrence of the landslides. Specifically, most of the landslides were shallow landslides, rockfalls and rock avalanches and were small in scale. The failure modes of landslides mainly include wedge rock mass failure, residual deposit failure, relaxed rock mass failure and weathered rock mass failure. The initial low stability of the dam coupled with the topographic effect, back-slope effect and excess pore water pressure led to damage to the Nuorilang Waterfall dam.

The Vulnerability of People to Damaging Hydrogeological Events in the Calabria Region (Southern Italy)

Background: Damaging Hydrogeological Events (DHEs) are severe weather periods during which floods, landslides, lightning, windstorms, hail or storm surges can harm people. Climate change is expected to increase the frequency/intensity of DHEs and, consequently, the potential harm to people. Method: We investigated the impacts of DHEs on people in Calabria (Italy) over 37 years (1980-2016). Data on 7288 people physically affected by DHEs were gathered from the systematic analysis of regional newspapers and collected in the database named PEOPLE. The damage was codified in three severity levels as follows: fatalities (people who were killed), injured (people who suffered physical harm) and involved (people who were present at the place where an accident occurred but survived and were not harmed). During the study period, we recorded 68 fatalities, 566 injured and 6654 people involved in the events. Results: Males were more frequently killed, injured and involved than females, and females who suffered fatalities were older than males who suffered fatalities, perhaps indicating that younger females tended to be more cautious than same-aged males, while older females showed an intrinsic greater vulnerability. Involved people were younger than injured people and fatalities, suggesting that younger people show greater promptness in reacting to dangerous situations. Floods caused the majority of the fatalities, injured and involved people, followed by landslides. Lightning was the most dangerous phenomenon, and it affected a relatively low number of people, killing 11.63% of them and causing injuries to 37.2%. Fatalities and injuries mainly occurred outdoors, largely along roads. In contrast, people indoors, essentially in public or private buildings, were more frequently involved without suffering harm. Being "dragged by water/mud" and "surrounded by water/mud", respectively, represented the two extremes of dynamic dangerousness. The dragging effect of rapid-flowing water totally or partially obstructed the attempts of people to save their lives. In contrast, people surrounded by steady water/mud encountered difficulties but ultimately could survive. Conclusions: The study outcomes can be used in informational campaigns to increase risk awareness among both administrators and citizens and to improve community resilience, particularly in promoting self-protective behaviors and avoiding the underestimation of hazardous situations.

DNA Identification of Commingled Human Remains from the Cemetery Relocated by Flooding in Central Bosnia and Herzegovina

The floods in Bosnia and Herzegovina in May 2014 caused landslides all over the country. In the small village of Šerići, near the town of Zenica, a landslide destroyed the local cemetery, relocated graves, and commingled skeletal remains. As the use of other physical methods of identification (facial recognition, fingerprint analysis, dental analysis, etc.) was not possible, DNA analysis was applied. DNA was isolated from 20 skeletal remains (bone and tooth samples) and six reference samples (blood from living relatives) and amplified using PowerPlex^® Fusion and PowerPlex^® Y23 kits. DNA profiles were generated for all reference samples and 17 skeletal remains. A statistical analysis (calculation of paternity, maternity, and sibling indexes and matching probabilities) resulted in 10 positive identifications. In this study, 5 individuals were identified based on one reference sample. This has once again demonstrated the significance of DNA analysis in resolving the most complicated cases, such as the identification of commingled human skeletal remains.

A Remote Sensing-Based Tool for Assessing Rainfall-Driven Hazards

RainyDay is a Python-based platform that couples rainfall remote sensing data with Stochastic Storm Transposition (SST) for modeling rainfall-driven hazards such as floods and landslides. SST effectively lengthens the extreme rainfall record through temporal resampling and spatial transposition of observed storms from the surrounding region to create many extreme rainfall scenarios. Intensity-Duration-Frequency (IDF) curves are often used for hazard modeling but require long records to describe the distribution of rainfall depth and duration and do not provide information regarding rainfall space-time structure, limiting their usefulness to small scales. In contrast, RainyDay can be used for many hazard applications with 1-2 decades of data, and output rainfall scenarios incorporate detailed space-time structure from remote sensing. Thanks to global satellite coverage, RainyDay can be used in inaccessible areas and developing countries lacking ground measurements, though results are impacted by remote sensing errors. RainyDay can be useful for hazard modeling under nonstationary conditions.

A High Performance Piezoelectric Sensor for Dynamic Force Monitoring of Landslide

Due to the increasing influence of human engineering activities, it is important to monitor the transient disturbance during the evolution process of landslide. For this purpose, a high-performance piezoelectric sensor is presented in this paper. To adapt the high static and dynamic stress environment in slope engineering, two key techniques, namely, the self-structure pressure distribution method (SSPDM) and the capacitive circuit voltage distribution method (CCVDM) are employed in the design of the sensor. The SSPDM can greatly improve the compressive capacity and the CCVDM can quantitatively decrease the high direct response voltage. Then, the calibration experiments are conducted via the independently invented static and transient mechanism since the conventional testing machines cannot match the calibration requirements. The sensitivity coefficient is obtained and the results reveal that the sensor has the characteristics of high compressive capacity, stable sensitivities under different static preload levels and wide-range dynamic measuring linearity. Finally, to reduce the measuring error caused by charge leakage of the piezoelectric element, a low-frequency correction method is proposed and experimental verified. Therefore, with the satisfactory static and dynamic properties and the improving low-frequency measuring reliability, the sensor can complement dynamic monitoring capability of the existing landslide monitoring and forecasting system.

Rate-weakening friction characterizes both slow sliding and catastrophic failure of landslides

Catastrophic landslides cause billions of dollars in damages and claim thousands of lives annually, whereas slow-moving landslides with negligible inertia dominate sediment transport on many weathered hillslopes. Surprisingly, both failure modes are displayed by nearby landslides (and individual landslides in different years) subjected to almost identical environmental conditions. Such observations have motivated the search for mechanisms that can cause slow-moving landslides to transition via runaway acceleration to catastrophic failure. A similarly diverse range of sliding behavior, including earthquakes and slow-slip events, occurs along tectonic faults. Our understanding of these phenomena has benefitted from mechanical treatments that rely upon key ingredients that are notably absent from previous landslide descriptions. Here, we describe landslide motion using a rate- and state-dependent frictional model that incorporates a nonlocal stress balance to account for the elastic response to gradients in slip. Our idealized, one-dimensional model reproduces both the displacement patterns observed in slow-moving landslides and the acceleration toward failure exhibited by catastrophic events. Catastrophic failure occurs only when the slip surface is characterized by rate-weakening friction and its lateral dimensions exceed a critical nucleation length [Formula: see text] that is shorter for higher effective stresses. However, landslides that are extensive enough to fall within this regime can nevertheless slide slowly for months or years before catastrophic failure. Our results suggest that the diversity of slip behavior observed during landslides can be described with a single model adapted from standard fault mechanics treatments.

Coping Strategies for Landslide and Flood Disasters: A Qualitative Study of Mt. Elgon Region, Uganda

INTRODUCTION

The occurrence of landslides and floods in East Africa has increased over the past decades with enormous Public Health implications and massive alterations in the lives of those affected. In Uganda, the Elgon region is reported to have the highest occurrence of landslides and floods making this area vulnerable. This study aimed at understanding both coping strategies and the underlying causes of vulnerability to landslides and floods in the Mt. Elgon region.

METHODS

We conducted a qualitative study in three districts of Bududa, Manafwa and Butalejja in the Mt. Elgon region in eastern Uganda. Six Focus Group Discussions (FGDs) and eight Key Informant Interviews (KIIs) were conducted. We used trained research assistants (moderator and note taker) to collect data. All discussions were audio taped, and were transcribed verbatim before analysis. We explored both coping strategies and underlying causes of vulnerability. Data were analysed using latent content analysis; through identifying codes from which basis categories were generated and grouped into themes.

RESULTS

The positive coping strategies used to deal with landslides and floods included adoption of good farming methods, support from government and other partners, livelihood diversification and using indigenous knowledge in weather forecasting and preparedness. Relocation was identified as unsustainable because people often returned back to high risk areas. The key underlying causes of vulnerability were; poverty, population pressure making people move to high risk areas, unsatisfactory knowledge on disaster preparedness and, cultural beliefs affecting people's ability to cope.

CONCLUSION

This study revealed that deep rooted links to poverty, culture and unsatisfactory knowledge on disaster preparedness were responsible for failure to overcome the effects to landslides and floods in disaster prone communities of Uganda. However, good farming practices and support from the government and implementation partners were shown to be effective in enabling the community to lessen the negative effects disasters. This calls for high impact innovative interventions focused in addressing these underlying causes as well as involvement of all stakeholders in scaling the effective coping strategies in order to build resilience in this community and other similarly affected areas.

KEY WORDS

Coping, Underlying causes, Floods, Landslides, Mt. Elgon, Uganda.

Mortality Patterns of Hydro-Geomorphologic Disasters

Social impacts caused by floods and landslides in Portugal in the period of 1865-2010 are gathered in the DISASTER database. This database contains 1,902 hydro-geomorphologic cases that caused 1,248 fatalities (81% and 19% associated with floods and landslides, respectively). The use of the DISASTER database allowed for: (i) the analysis of the frequency and the temporal evolution of fatal floods and landslides; (ii) the analysis of the spatio-temporal distribution of fatalities; (iii) the identification of the most deadly flood and landside types; (iv) the verification of gender tendencies in mortalities; and (v) the evaluation of individual and societal risk. The highest number of flood and landslide cases and related mortalities occurred in the period of 1935-1969. After this period, the number of flood and landslide mortalities decreased, although landslide fatalities remained higher than those registered in the period of 1865-1934. The occurrence of flood fatalities was widespread in the country, with an important cluster in the Lisbon region and in the Tagus valley, while fatalities caused by landslides mainly occurred in the north of the Tagus valley. Flash floods caused the majority of fatalities associated with floods, while falls and flows were responsible for the highest number of fatalities associated with landslides. Males were found to have the highest frequency of fatalities. In the case of floods, the majority of fatalities were found both outdoors and inside of buildings in rural areas while fatalities inside buildings were dominant in landslide cases, mostly in rural areas.

Climate-Induced Landslides within the Larch Dominant Permafrost Zone of Central Siberia

Climate impact on landslide occurrence and spatial patterns were analyzed within the larch-dominant communities associated with continuous permafrost areas of Central Siberia. We used high resolution satellite imagery (i.e. QuickBird, WorldView) to identify landslide scars over an area of 62000 km². Landslide occurrence was analyzed with respect to climate variables (air temperature, precipitation, drought index SPEI), and GRACE satellite derived equivalent of water thickness anomalies (EWTA). Landslides were found only on southward facing slopes, and the occurrence of landslides increased exponentially with increasing slope steepness. Lengths of landslides correlated positively with slope steepness. The observed upper elevation limit of landslides tended to coincide with the tree line. Observations revealed landslides occurrence was also found to be strongly correlated with August precipitation (r = 0.81) and drought index (r = 0.7), with June-July-August soil water anomalies (i.e., EWTA, r = 0.68-0.7), and number of thawing days (i.e., a number of days with t_max > 0°C; r = 0.67). A significant increase in the variance of soil water anomalies was observed, indicating that occurrence of landslides may increase even with a stable mean precipitation level. The key-findings of this study are (1) landslides occurrence increased within the permafrost zone of Central Siberia in the beginning of the 21st century; (2) the main cause of increased landslides occurrence are extremes in precipitation and soil water anomalies; and (3) landslides occurrence are strongly dependent on relief features such as southward facing steep slopes.

Injury Patterns After the Landslide Disaster in Oshima, Tokyo, Japan on October 16, 2013

INTRODUCTION

Landslides represent a frequent and threatening natural disaster. The aim of this study was to investigate the injury patterns observed after a landslide and to discuss how to minimize the damage caused by a landslide disaster.

METHODS

A landslide occurred on Oshima Island, Japan, on October 16, 2013. A total of 49 victims with landslide-related injuries were identified and analyzed.

RESULTS

The patients ranged in age from 5 to 89 years with an average age of 61.0±19.3 years. Of all patients, 69.4% were triaged as black. Of 15 patients who were treated in the nearest hospital (the only hospital on the island), 8 were triaged as red and yellow with severe chest or pelvic injury and a high Injury Severity Score (average score, 25.6; range, 4-45). Of these, 75% had chest injury and 75% had pelvic injury. The percentage of chest and/or pelvic injury was 100% in patients triaged as red or yellow. Traumatic asphyxia was diagnosed in 62.5% of these patients.

CONCLUSIONS

Compression of the trunk was the main injury in patients triaged as red or yellow after this landslide disaster. Evacuation in advance, the rapid launch of emergency medical support, and knowledge of this specific injury pattern are essential to minimize the potential damage resulting from landslide disasters.

On the characteristics of landslide tsunamis

This review presents modelling techniques and processes that govern landslide tsunami generation, with emphasis on tsunamis induced by fully submerged landslides. The analysis focuses on a set of representative examples in simplified geometries demonstrating the main kinematic landslide parameters influencing initial tsunami amplitudes and wavelengths. Scaling relations from laboratory experiments for subaerial landslide tsunamis are also briefly reviewed. It is found that the landslide acceleration determines the initial tsunami elevation for translational landslides, while the landslide velocity is more important for impulsive events such as rapid slumps and subaerial landslides. Retrogressive effects stretch the tsunami, and in certain cases produce enlarged amplitudes due to positive interference. In an example involving a deformable landslide, it is found that the landslide deformation has only a weak influence on tsunamigenesis. However, more research is needed to determine how landslide flow processes that involve strong deformation and long run-out determine tsunami generation.

Disturbance regimes, gap-demanding trees and seed mass related to tree height in warm temperate rain forests worldwide

For tropical lowland rain forests, Denslow (1987) hypothesized that in areas with large-scale disturbances tree species with a high demand for light make up a larger proportion of the flora; results of tests have been inconsistent. There has been no test for warm temperate rain forests (WTRFs), but they offer a promising testing ground because they differ widely in the extent of disturbance. WTRF is dominated by microphylls sensu Raunkiaer and has a simpler structure and range of physiognomy than tropical or subtropical rain forests. It occurs in six parts of the world: eastern Asia, New Zealand, Chile, South Africa, SE Australia and the Azores. On the Azores it has been mostly destroyed, so we studied instead the subtropical montane rain forest (STMRF) on the Canary Islands which also represents a relict of the kind of WTRF that once stretched across southern Eurasia. We sought to find whether in these six regions the proportion of tree species needing canopy gaps for establishment reflects the frequency and/or extent of canopy disturbance by wind, landslide, volcanic eruptions (lava flow and ash fall), flood or fire. We used standard floras and ecological accounts to draw up lists of core tree species commonly reaching 5 m height. We excluded species which are very rare, very localized in distribution, or confined to special habitats, e.g. coastal forests or rocky sites. We used published accounts and our own experience to classify species into three groups: (1) needing canopy gaps for establishment; (2) needing either light shade throughout or a canopy gap relatively soon (a few months or years) after establishment; and (3) variously more shade-tolerant. Group 1 species were divided according the kind of canopy opening needed: tree-fall gap, landslide, lava flow, flood or fire. Only some of the significant differences in proportion of Group 1 species were consistent with differences in the extent of disturbance; even in some of those cases other factors seem likely to have had a major determining influence during evolution. We also sought to determine whether the species that are at least 'short-term persistent' in the soil seed bank (lasting 2-4 years) are all species needing canopy gaps for establishment. The answer was negative; large numbers of seeds of some shade-tolerants accumulate in the soil, and these species are able to benefit from soil disturbance in deep shade. We found a significant and strong positive relationship in Japan between mean seed mass and mature tree height, a weak positive relationship in New Zealand and no relationship in any of the other four regions. When comparing the seed mass values of Group 1 and Group 3 species we obtained different answers depending on whether or not we confined ourselves to taxonomically controlled contrasts. In only two of the four regions with an appreciable number of species in Group 1 is the mean seed mass of such species significantly lower than that of Group 3 species when taxonomic relatedness is ignored.

Integrating Remote Sensing Data with Directional Two- Dimensional Wavelet Analysis and Open Geospatial Techniques for Efficient Disaster Monitoring and Management

In Taiwan, earthquakes have long been recognized as a major cause oflandslides that are wide spread by floods brought by typhoons followed. Distinguishingbetween landslide spatial patterns in different disturbance regimes is fundamental fordisaster monitoring, management, and land-cover restoration. To circumscribe landslides,this study adopts the normalized difference vegetation index (NDVI), which can bedetermined by simply applying mathematical operations of near-infrared and visible-redspectral data immediately after remotely sensed data is acquired. In real-time disastermonitoring, the NDVI is more effective than using land-cover classifications generatedfrom remotely sensed data as land-cover classification tasks are extremely time consuming.Directional two-dimensional (2D) wavelet analysis has an advantage over traditionalspectrum analysis in that it determines localized variations along a specific direction whenidentifying dominant modes of change, and where those modes are located in multi-temporal remotely sensed images. Open geospatial techniques comprise a series ofsolutions developed based on Open Geospatial Consortium specifications that can beapplied to encode data for interoperability and develop an open geospatial service for sharing data. This study presents a novel approach and framework that uses directional 2Dwavelet analysis of real-time NDVI images to effectively identify landslide patterns andshare resulting patterns via open geospatial techniques. As a case study, this study analyzedNDVI images derived from SPOT HRV images before and after the ChiChi earthquake(7.3 on the Richter scale) that hit the Chenyulan basin in Taiwan, as well as images aftertwo large typhoons (Xangsane and Toraji) to delineate the spatial patterns of landslidescaused by major disturbances. Disturbed spatial patterns of landslides that followed theseevents were successfully delineated using 2D wavelet analysis, and results of patternrecognitions of landslides were distributed simultaneously to other agents using geographymarkup language. Real-time information allows successive platforms (agents) to work withlocal geospatial data for disaster management. Furthermore, the proposed is suitable fordetecting landslides in various regions on continental, regional, and local scales usingremotely sensed data in various resolutions derived from SPOT HRV, IKONOS, andQuickBird multispectral images.

Root system architecture of Quercus pubescens trees growing on different sloping conditions

BACKGROUND AND AIMS

Plant roots' growth direction has important implications for plant development and survival; moreover it plays an effective and vital role in stabilizing weathered soil on a steep slope. The aim of this work was to assess the influence of slope on the architecture of woody root systems.

METHODS

Five mature, single-stemmed Quercus pubescens trees growing on a steep slope and five on a shallow slope were excavated to a root diameter of 1 cm. A very precise numeric representation of the geometry and topology of structural root architecture was gained using a low-magnetic-field digitizing device (Fastrak, Polhemus). Several characteristics of root architecture were extracted by macros, including root volume, diameter, length, number, spatial position and branching order.

KEY RESULTS

The diameter at breast height (dbh) was the best predictor of the root volume but had no correlation with length and number of roots. The slope affected the root volume for each branching order, and the basal cross-sectional area (CSA), number and length of the first-order roots. Number and length of the second- and third-order laterals were closely related in both conditions, although this relationship was closer in the shallow trees, suggesting the influence of a genetic control. Sloping trees showed a clustering tendency of the first- and second-order lateral roots in the up-slope direction, suggesting that the laterals rather than the taproots provide much of the anchorage. In a steep-slope condition, the taproot tapering was positively correlated with the asymmetry magnitude of first-order roots, indicating compensation between taproot and main lateral roots' clustering tendency.

CONCLUSIONS

These results suggest that on a slope, on clayey soils, root asymmetry appears to be a consequence of several environmental factors such as inclination, shallow-slides and soil compactness. In addition, this adaptive growth seems to counteract the turning moment induced by the self-loading forces acting in slope conditions, and as a consequence improves the tree stability.