Soil erosion in the Alps : causes and risk assessment

The issue of soil erosion in the Alps has long been neglected due to the low economic value of the agricultural land. However, soil stability is a key parameter which affects ecosystem services like slope stability, water budgets (drinking water reservoirs as well as flood prevention), vegetation productivity, ecosystem biodiversity and nutrient production. In alpine regions, spatial estimates on soil erosion are difficult to derive because the highly heterogeneous biogeophysical structure impedes measurement of soil erosion and the applicability of soil erosion models. However, remote sensing and geographic information system (GIS) methods allow for spatial estimation of soil erosion by direct detection of erosion features and supply of input data for soil erosion models. Thus, the main objective of this work is to address the problem of soil erosion risk assessment in the Alps on catchment scale with remote sensing and GIS tools. Regarding soil erosion processes the focus is on soil erosion by water (here sheet erosion) and gravity (here landslides). For these two processes we address i) the monitoring and mapping of the erosion features and related causal factors ii) soil erosion risk assessment with special emphasis on iii) the validation of existing models for alpine areas. All investigations were accomplished in the Urseren Valley (Central Swiss Alps) where the valley slopes are dramatically affected by sheet erosion and landslides. For landslides, a natural susceptibility of the catchment has been indicated by bivariate and multivariate statistical analysis. Geology, slope and stream density are the most significant static landslide causal factors. Static factors are here defined as factors that do not change their attributes during the considered time span of the study (45 years), e.g. geology, stream network. The occurrence of landslides might be significantly increased by the combined effects of global climate and land use change. Thus, our hypothesis is that more recent changes in land use and climate affected the spatial and temporal occurrence of landslides. The increase of the landslide area of 92% within 45 years in the study site confirmed our hypothesis. In order to identify the cause for the trend in landslide occurrence time-series of landslide causal factors were analysed. The analysis revealed increasing trends in the frequency and intensity of extreme rainfall events and stocking of pasture animals. These developments presumably enhanced landslide hazard. Moreover, changes in land-cover and land use were shown to have affected landslide occurrence. For instance, abandoned areas and areas with recently emerging shrub vegetation show very low landslide densities. Detailed spatial analysis of the land use with GIS and interviews with farmers confirmed the strong influence of the land use management practises on slope stability. The definite identification and quantification of the impact of these non-stationary landslide causal factors (dynamic factors) on the landslide trend was not possible due to the simultaneous change of several factors. The consideration of dynamic factors in statistical landslide susceptibility assessments is still unsolved. The latter may lead to erroneous model predictions, especially in times of dramatic environmental change. Thus, we evaluated the effect of dynamic landslide causal factors on the validity of landslide susceptibility maps for spatial and temporal predictions. For this purpose, a logistic regression model based on data of the year 2000 was set up. The resulting landslide susceptibility map was valid for spatial predictions. However, the model failed to predict the landslides that occurred in a subsequent event. In order to handle this weakness of statistic landslide modelling a multitemporal approach was developed. It is based on establishing logistic regression models for two points in time (here 1959 and 2000). Both models could correctly classify >70% of the independent spatial validation dataset. By subtracting the 1959 susceptibility map from the 2000 susceptibility map a deviation susceptibility map was obtained. Our interpretation was that these susceptibility deviations indicate the effect of dynamic causal factors on the landslide probability. The deviation map explained 85% of new independent landslides occurring after 2000. Thus, we believe it to be a suitable tool to add a time element to a susceptibility map pointing to areas with changing susceptibility due to recently changing environmental conditions or human interactions. In contrast to landslides that are a direct threat to buildings and infrastructure, sheet erosion attracts less attention because it is often an unseen process. Nonetheless, sheet erosion may account for a major proportion of soil loss. Soil loss by sheet erosion is related to high spatial variability, however, in contrast to arable fields for alpine grasslands erosion damages are long lasting and visible over longer time periods. A crucial erosion triggering parameter that can be derived from satellite imagery is fractional vegetation cover (FVC). Measurements of the radiogenic isotope Cs-137, which is a common tracer for soil erosion, confirm the importance of FVC for soil erosion yield in alpine areas. Linear spectral unmixing (LSU), mixture tuned matched filtering (MTMF) and the spectral index NDVI are applied for estimating fractional abundance of vegetation and bare soil. To account for the small scale heterogeneity of the alpine landscape very high resolved multispectral QuickBird imagery is used. The performance of LSU and MTMF for estimating percent vegetation cover is good (r²=0.85, r²=0.71 respectively). A poorer performance is achieved for bare soil (r²=0.28, r²=0.39 respectively) because compared to vegetation, bare soil has a less characteristic spectral signature in the wavelength domain detected by the QuickBird sensor. Apart from monitoring erosion controlling factors, quantification of soil erosion by applying soil erosion risk models is done. The performance of the two established models Universal Soil Loss Equation (USLE) and Pan-European Soil Erosion Risk Assessment (PESERA) for their suitability to model erosion for mountain environments is tested. Cs-137 is used to verify the resulting erosion rates from USLE and PESERA. PESERA yields no correlation to measured Cs-137 long term erosion rates and shows lower sensitivity to FVC. Thus, USLE is used to model the entire study site. The LSU-derived FVC map is used to adapt the C factor of the USLE. Compared to the low erosion rates computed with the former available low resolution dataset (1:25000) the satellite supported USLE map shows “hotspots” of soil erosion of up to 16 t ha-1 a-1. In general, Cs-137 in combination with the USLE is a very suitable method to assess soil erosion for larger areas, as both give estimates on long-term soil erosion. Especially for inaccessible alpine areas, GIS and remote sensing proved to be powerful tools that can be used for repetitive measurements of erosion features and causal factors. In times of global change it is of crucial importance to account for temporal developments. However, the evaluation of the applied soil erosion risk models revealed that the implementation of temporal aspects, such as varying climate, land use and vegetation cover is still insufficient. Thus, the proposed validation strategies (spatial, temporal and via Cs-137) are essential. Further case studies in alpine regions are needed to test the methods elaborated for the Urseren Valley. However, the presented approaches are promising with respect to improve the monitoring and identification of soil erosion risk areas in alpine regions

Geospatial methods and tools for natural risk management and communications

In the last decade, real-time access to data and the use of high-resolution spatial information have provided scientists and engineers with valuable information to help them understand risk. At the same time, there has been a rapid growth of novel and cutting-edge information and communication technologies for the collection, analysis and dissemination of data, re-inventing the way in which risk management is carried out throughout its cycle (risk identification and reduction, preparedness, disaster relief and recovery). The applications of those geospatial technologies are expected to enable better mitigation of, and adaptation to, the disastrous impact of natural hazards. The description of risks may particularly benefit from the integrated use of new algorithms and monitoring techniques. The ability of new tools to carry out intensive analyses over huge datasets makes it possible to perform future risk assessments, keeping abreast of temporal and spatial changes in hazard, exposure, and vulnerability. The present special issue aims to describe the state-of-the-art of natural risk assessment, management, and communication using new geospatial models and Earth Observation (EO)architecture. More specifically, we have collected a number of contributions dealing with: (1) applications of EO data and machine learning techniques for hazard, vulnerability and risk mapping; (2) natural hazards monitoring and forecasting geospatial systems; (3) modeling of spatiotemporal resource optimization for emergency management in the post-disaster phase; and (4) development of tools and platforms for risk projection assessment and communication of inherent uncertainties

Storm-triggered landslides in the Peruvian Andes and implications for topography, carbon cycles, and biodiversity

In this study, we assess the geomorphic role of a rare, large-magnitude landslide-triggering event and consider its effect on mountain forest ecosystems and the erosion of organic carbon in an Andean river catchment. Proximal triggers such as large rain storms are known to cause large numbers of landslides, but the relative effects of such low-frequency, high-magnitude events are not well known in the context of more regular, smaller events. We develop a 25-year duration, annual-resolution landslide inventory by mapping landslide occurrence in the Kosñipata Valley, Peru, from 1988 to 2012 using Landsat, QuickBird, and WorldView satellite images. Catchment-wide landslide rates were high, averaging 0.076 % yr−1 by area. As a result, landslides on average completely turn over hillslopes every  ∼  1320 years, although our data suggest that landslide occurrence varies spatially and temporally, such that turnover times are likely to be non-uniform. In total, landslides stripped 26 ± 4 tC km−2 yr−1 of organic carbon from soil (80 %) and vegetation (20 %) during the study period. A single rain storm in March 2010 accounted for 27 % of all landslide area observed during the 25-year study and accounted for 26 % of the landslide-associated organic carbon flux. An approximately linear magnitude–frequency relationship for annual landslide areas suggests that large storms contribute an equivalent landslide failure area to the sum of lower-frequency landslide events occurring over the same period. However, the spatial distribution of landslides associated with the 2010 storm is distinct. On the basis of precipitation statistics and landscape morphology, we hypothesise that focusing of storm-triggered landslide erosion at lower elevations in the Kosñipata catchment may be characteristic of longer-term patterns. These patterns may have implications for the source and composition of sediments and organic material supplied to river systems of the Amazon Basin, and, through focusing of regular ecological disturbance, for the species composition of forested ecosystems in the region

LANDSLIDE SUSCEPTIBILITY MODELLING UNDER ENVIRONMENTAL CHANGES: A CASE STUDY OF CAMERON HIGHLANDS, MALAYSIA

Modeling landslide susceptibility usually does not include multi temporal factors, e.g. rainfall, especially for medium scale. Landslide occurrences in Cameron Highlands, in particular, and in Peninsular Malaysia, in general, tend to increase during the peak times of monsoonal rainfall. Due to the lack of high spatial resolution of rainfall data, Normalized Different Vegetation Index (NDVI), soil wetness, and LST (Land Surface Temperature) were selected as replacement of multi temporal rainfall data. This research investigated their roles in landslide susceptibility modeling. In doing so, four Landsat 7 Enhanced Multi Temporal Plus (ETM+) images acquired during two peak times of rainy and dry seasons were used to derive multi temporal NDVI, soil wetness, and LST. Topographic, geology, and soil maps were used to derive ‘static’ factors namely slope, slope aspect, curvature, elevation, road network, river/lake, lithology, soil geology lineament maps. Landslide map was used to derive weighting system based on spatial relationship between landslide occurrences and landslide factor using bivariate statistical method. A non-statistical weighting system was also used for comparison purpose. Different scenarios of data processing were applied to allow evaluation on the roles of multi temporal factors in landslide susceptibility modeling in terms of the accuracy of the landslide susceptibility maps (LSMs), the appropriate weighting system of the models, the applicability of the model, the ability to confirm the relation between landslide occurrences and rainfall. The results show that the average accuracy of LSMs produced by the developed models with inclusion of multi temporal factors is 49.1% on the overall. Addition of LST tends to improve the accuracy of LSMs. NDVI can be a suitable replacement for rainfall data since it can explain the relation between landslides occurrences and rainfall cycle. Statistical-based weighting system produced more accurate LSMs than non-statistical-based one and is applicable for landslide susceptibility modeling elsewhere. Significant causative factors were proven to produce more accurate LSMs

Risk information services for Disaster Risk Management (DRM) in the Caribbean : operational documentation

The primary objective of this ESA project is to raise awareness within the World Bank (WB) of the capabilities of Earth Observation (EO) data and specialist service providers to supply information customised to the specific needs of individual projects. This project was set up within the ESA/WB eoworld initiative to contribute to the WB Caribbean Risk Information Program that is operating under a grant from the ACP-EU Natural Risk Reduction Program. The Caribbean is heavily affected by natural (and geo-) hazards with over 5 billion US$in losses in the last 20 years (source: CRED database). Figure 1 illustrates the division of natural disaster by occurrence in the region over the last 30 years, providing an insight into the impact in the region over a significant time period. A specific example of the environmental, social, economic and political issues that the project is addressing is highlighted by the effects of Hurricane Tomas on St Lucia in October 2010. The hurricane resulted in seven deaths with 5952 people severely affected, while the cost of the damage was estimated at US$336.2 million, representing 43.4% of GDP (ECLAC, 2011). Understanding and mitigating these “geo-environmental disasters” (as they are termed in ECLAC, 2011) is a primary concern in the region

Recommendations for the quantitative analysis of landslide risk

This paper presents recommended methodologies for the quantitative analysis of landslide hazard, vulnerability and risk at different spatial scales (site-specific, local, regional and national), as well as for the verification and validation of the results. The methodologies described focus on the evaluation of the probabilities of occurrence of different landslide types with certain characteristics. Methods used to determine the spatial distribution of landslide intensity, the characterisation of the elements at risk, the assessment of the potential degree of damage and the quantification of the vulnerability of the elements at risk, and those used to perform the quantitative risk analysis are also described. The paper is intended for use by scientists and practising engineers, geologists and other landslide experts.JRC.H.5-Land Resources Managemen

Assessment of the performance of X-band satellite radar data for landslide mapping and monitoring: Upper Tena Valley case study.

Abstract. The aim of this work is to analyse the advantages and disadvantages of using the new X-band SAR data acquired by TerraSAR-X sensors for landslides mapping. This dataset has been processed using a Persistent Scatterer Interferometry technique over the Upper Tena Valley (Central Pyrenees, Spain). In the first section, the geological and geomorphological setting of the study area is introduced, focusing on the description of the landslide inventory. Then the Stable Point Network technique is briefly described, followed by the assessment of the performance of the X-band SAR dataset. In this context, we present first a model to predict the distribution of Persistent Scatterers based on the slope geometry and the land use information, which has then been validated with X-band data results. On a second stage, we have assessed the performance of X-band dataset to detect and monitor mapped landslides. Finally some illustrative case studies are shown demonstrating the potential of using X-band SAR data not only for landslide mapping but also to detect and monitor deformations affecting human infrastructures

Use of LIDAR in landslide investigations: a review

This paper presents a short history of the appraisal of laser scanner technologies in geosciences used for imaging relief by high-resolution digital elevation models (HRDEMs) or 3D models. A general overview of light detection and ranging (LIDAR) techniques applied to landslides is given, followed by a review of different applications of LIDAR for landslide, rockfall and debris-flow. These applications are classified as: (1) Detection and characterization of mass movements; (2) Hazard assessment and susceptibility mapping; (3) Modelling; (4) Monitoring. This review emphasizes how LIDAR-derived HRDEMs can be used to investigate any type of landslides. It is clear that such HRDEMs are not yet a common tool for landslides investigations, but this technique has opened new domains of applications that still have to be develope

Changing Significance of Landslide Hazard and Risk After The 2015 Mw 7.8 Gorkha, Nepal Earthquake

The 2015 Mw 7.8 Gorkha, Nepal Earthquake triggered in excess of 20,000 landslides across 14 districts of Central and Western Nepal. Whilst the instantaneous impact of these landslides was significant, the ongoing effect of the earthquake on changing the potential for rainfall-triggered landsliding in the months and years that followed has remained poorly understood and challenging to predict. To provide insight into how landsliding has evolved since the earthquake, and how it has impacted those living in the affected area, a detailed time-series landslide mapping campaign was undertaken to monitor the evolution of coseismic landslides and the initiation of new post-seismic landslides. This was supplemented by numerical modelling to simulate the future potential reactivation and runout of landslides as debris flows under monsoon rainfall, identifying locations potentially at risk. This analysis shows that landslide hazard was higher in November 2019 as compared to immediately after the 2015 earthquake, with a considerable portion of the landscape being impacted by landsliding. We show that, while pre-existing landslides continued to pose the majority of hazard in the aftermath of the earthquake, a significant number of landslides also occurred in new locations. We discuss the value of this type of analysis in informing the reconstruction and management of settlements at risk by summarizing how this work was integrated into the project Durable Solutions II, that supported communities at risk from landslides. Finally, we consider how such data could be used in future to inform risk sensitive land-use planning and disaster recovery, and to mitigate the impacts of future landsliding in Nepal and beyond