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

Predicting the Spatial Distribution of Rain-Induced Shallow Landslides by applying GIS and Geocomputational Techniques: A Case Study from North East India

This study presents a case of statistical modelling, by applying GIS and geocomputational techniques, to predict areas that are susceptible to future rain-induced shallow landslides. The statistical prediction model is based on the observed relationships between the spatial distribution of past landslideevents and environmental (causal) factors that are associated with such phenomena. The study also evaluates the predictive performance of a nonlinear regression model, namely the Generalized Additive Model(GAM),applied for the analysis. The study area comprises a residual hill of ? 6 Km2 area situated in the heart of Guwahati (capital city of Assam in NE India). We exploited the geoprocessing functions of SAGA GIS to derive nine different terrain attributesfrom a digital elevation model (DEM) processed by synthetic aperture radar interferometry (InSAR). The terrain attributes along with land use classes, in raster grid format, constitute the predictor variables. An inventory of the locations of eighty-two past occurrences of shallow landslide events constitutes the response. We performed the modelling and statistical geocomputation entirely in the open-source R language and software environment. The procedure comprises the following three steps: (1) Collinearityanalysis to discard redundant predictors. (2) 100-fold bootstrap resampling to fit the GAM by a random selection of 2/3 of the landslide pixels ("training" subset) and validate the GAM by the remaining 1/3 ("test" subset). (3) Estimate model accuracy (true error rates) by a repeated 100-fold 'hold-out validation' method and evaluate the predictive performance of the model by the Area under the ROC curve (AUROC) computed for 100 independently trained models. The mean and standard deviation of accuracy on training sets are 0.80 and 0.01, and that on test sets are 0.79 and 0.02 respectively. The AUROC corresponding to the meanof landslide probabilities is 0.87, and that of the 95% Confidence Intervals (CI) is between 0.86 and 0.88. Thevalues of these quality measures indicate that a data-driven model, such as the GAM, is efficient regarding its predictive performance, to highlight the unstable areas in the study area. We subsequently used the mean values of the landslide probability (susceptibility) estimates corresponding to each mapping unit (grid cell) to construct the landslide susceptibility map, which can be used for land use planning and hazard mitigation

Automatic landslide detection using Dempster–Shafer theory from LiDAR-derived data and orthophotos

© 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. A good landslide inventory map is a prerequisite for landslide hazard and risk analysis. In tropical countries, such as Malaysia, preparation of the landslide inventory is a challenging task because of the rapid growth of vegetation. Thus, it is crucial to use rapid and accurate technique and effective parameters. For this purpose, Dempster Shafer theory (DST) was applied in fusing high resolution LiDAR derived data products and Greenness index derived from orthophoto imagery. Two sites were selected, for the implementation and evaluation of the DST model; site “A” for DST implementation and site “B” for the comparison. For model implementation, vegetation index, slope and height were used as effective parameters for identifying automatic landslide detection. Two type of DST based fusions were evaluated; (greenness and height) and (greenness and slope). Furthermore, validation techniques were used to validate the accuracy are confusion matrix and area under the curve. The overall accuracy of the first and second evaluated fusions were (73.4% and 84.33%), and area under the curve were (0.76 and 0.81) respectively. Additionally, the result was compared with Random Forest (RF) based detection approach. The results showed that DST does not require a priori knowledge

Novel Approaches in Landslide Monitoring and Data Analysis

Significant progress has been made in the last few years that has expanded the knowledge of landslide processes. It is, therefore, necessary to summarize, share and disseminate the latest knowledge and expertise. This Special Issue brings together novel research focused on landslide monitoring, modelling and data analysis

Combined landslide inventory and susceptibility assessment based on different mapping units: an example from the Flemish Ardennes, Belgium

For a 277 km<sup>2</sup> study area in the Flemish Ardennes, Belgium, a landslide inventory and two landslide susceptibility zonations were combined to obtain an optimal landslide susceptibility assessment, in five classes. For the experiment, a regional landslide inventory, a 10 m × 10 m digital representation of topography, and lithological and soil hydrological information obtained from 1:50 000 scale maps, were exploited. In the study area, the regional inventory shows 192 landslides of the slide type, including 158 slope failures occurred before 1992 (model calibration set), and 34 failures occurred after 1992 (model validation set). The study area was partitioned in 2.78×10<sup>6</sup> grid cells and in 1927 topographic units. The latter are hydro-morphological units obtained by subdividing slope units based on terrain gradient. Independent models were prepared for the two terrain subdivisions using discriminant analysis. For grid cells, a single pixel was identified as representative of the landslide depletion area, and geo-environmental information for the pixel was obtained from the thematic maps. The landslide and geo-environmental information was used to model the propensity of the terrain to host landslide source areas. For topographic units, morphologic and hydrologic information and the proportion of lithologic and soil hydrological types in each unit, were used to evaluate landslide susceptibility, including the depletion and depositional areas. Uncertainty associated with the two susceptibility models was evaluated, and the model performance was tested using the independent landslide validation set. An heuristic procedure was adopted to combine the landslide inventory and the susceptibility zonations. The procedure makes optimal use of the available landslide and susceptibility information, minimizing the limitations inherent in the inventory and the susceptibility maps. For the established susceptibility classes, regulations to link terrain domains to appropriate land rules are proposed

Lur labainketen analisirako hurbilketa metodologikoa eskala erregionalean: Datuen bilketa, suszeptibilitate modeloak eta euri prezipitazioen atalaseak. Gipuzkoako Lurralde Historikoan aplikatua (Euskal Herria).

227 p.Lurraldearen zonazioa lur labainketak jasotzeko aukeren arabera, suszeptibilitate mapen bitartez hain zuzen ere, hauek eragindako kalteak arindu eta mehatxua eta arrisku maila ebaluatu ahal izateko oinarrizko pausoa da. Tesi honek bide-orri baten definizioa aurkezten du, hutsetik abiatuta, lurraldearen suszeptibilitate mapen garapenerako eskala erregionalean. Helburua ikuspegi metodologiko eguneratu bat zehaztea da, prozeduraren pauso bakoitzean hartutako erabakia zientifikoki justifikatuak eta onartua izan daitezen. Hainbat esperimentu eta aplikazio gauzatzeko Gipuzkoako Probintzia hautatu da (1980 km2) . Ezaugarri eta izaera desberdineko hainbat aldagai independente, mota eta iturri desberdineko lur labainketa inbentarioak eta metodo ezagun nahiz metodo berritzaileekin batera ikuspegi desberdinak jorratu dira, azkenean lan honek aurkezten dituen ondorioak lortzeko.Emaitzen arabera, inferentzia geomorfologikoaren beharra azpimarratu daiteke estatistikoki gidatutako arauen arabera aldagai independenteen hautaketa egiterako orduan, hala nola, aldagai kategorikoen transformatzea aldagai jarraietan suszeptibilitate-modeloak garatzerako orduan onuragarria dela ere ondorioztatu da. Gainera, lur labainketen suszeptibilitate modeloak kalibratzeko ikuskatutako eremu efektiboaren erabilera positiboa dela frogatu da, lur labainketen inbentarioa landa lanaren bitartez eskuratu den kasuetan. Bestalde, malda unitateen erabilerak lurralde unitate bezala, ohikoak diren pixel unitateak izan beharrean, landa laneko inbentario batek ezarri dezakeen ziurgabetasuna arintzeko ahalmena erakutsi du.Horretaz gain, lur labainketak gertatzeko beharrezko prezipitazio atalasa definitzeko algoritmo baten aplikazioak ikerketa eremu berberean, aurreikuspenak egiteko beharrezkoa den informazioa erakutsi du, alerta goiztiar sistema baterantz aurreratzen joateko dauden aukerak goraipatuz

Comparing physically-based with data-driven models for landslide susceptibility: a case study in the Catalan Pyrenees

En este proyecto de investigación, se utilizaron un modelo físico (FSLAM) y cuatro modelos basados en datos (regresión logística, SVC, árbol de clasificación y bosque aleatorio) para mapear la susceptibilidad a los deslizamientos para un área de estudio ubicada en los Pirineos Catalanes. Seguidamente, se compararon los resultados de todos los modelos para determinar cuál funcionó mejor y en qué condiciones. También se discutieron las ventajas y desventajas de cada modelo, así como las limitaciones de sus productos finales.In this research project, a physically-based (FSLAM) and four data-driven models (logistic regression, SVC, classification tree and random forest) were used to map landslide susceptibility for a case study area located in the Catalan Pyrenees. The results for all models were then compared in order to determine which performed best and under which conditions. The advantages and disadvantages of each model were also discussed as well as the limitations of their end products

Shallow landslide susceptibility assessment in a data-poor region of Guatemala (Comitancillo Municipality)

Although landslides are frequent natural phenomena in mountainous regions, the lack of data in emerging countries is a significant issue in the assessment of shallow landslide susceptibility. A key factor in risk-mitigation strategies is the evaluation of deterministic physical models for hazard assessment in these data-poor regions. Given the lack of physical information, input parameters to these data-intensive deterministic models have to be estimated, which has a negative impact on the reliability of the assessment. To address this problem, we examined shallow landslide hazard in Comitancillo municipality, Guatemala. Shallow landslides are here defined as small (less than two or three metre-deep) rotational or translational slides or earth flows. We based our hazard simulation on the stability index mapping model. The model’s input parameters were estimated from a statistical analysis of factors affecting landslides in the municipality obtained from a geodatabase. The outputs from the model were analysed and compared to an inventory of small-scale landslides. The results of the comparison show the effectiveness of the method developed to estimate input parameters for a deterministic model, in regions where physical data related to the assessment of shallow landslide susceptibility is lacking

Dynamic rainfall-induced landslide susceptibility: A step towards a unified forecasting system

The initial inception of the landslide susceptibility concept defined it as a static property of the landscape, explaining the proneness of certain locations to generate slope failures. Since the spread of data-driven proba- bilistic solutions though, the original susceptibility definition has been challenged to incorporate dynamic ele- ments that would lead the occurrence probability to change both in space and in time. This is the starting point of this work, which combines the traditional strengths of the susceptibility framework together with the strengths typical of landslide early warning systems. Specifically, we model landslide occurrences in the norther sector of Vietnam, using a multi-temporal landslide inventory recently released by NASA. A set of static (terrain) and dynamic (cumulated rainfall) covariates are selected to explain the landslide presence/absence distribution via a Bayesian version of a binomial Generalized Additive Models (GAM). Thanks to the large spatiotemporal domain under consideration, we include a large suite of cross-validation routines, testing the landslide prediction through random sampling, as well as through stratified spatial and temporal sampling. We even extend the model test towards regions far away from the study site, to be used as external validation datasets. The overall per- formance appears to be quite high, with Area Under the Curve (AUC) values in the range of excellent model results, and very few localized exceptions. This model structure may serve as the basis for a new generation of early warning systems. However, the use of The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) for the rainfall component limits the model ability in terms of future prediction. Therefore, we envision subsequent development to take this direction and move towards a unified dynamic landslide forecast. Ultimately, as a proof-of-concept, we have also implemented a potential early warning system in Google Earth Engine