Shallow landslide susceptibility : modelling and validation

Rainfall frequently triggers shallow landslides in mountainous areas worldwide. Landslide susceptibility maps express the probability of occurrence of landslides based on terrain conditions; they are useful for disaster prevention and land use planning. This report is about val- idating a qualitative approach to map global landslide susceptibility, based on the weighted linear combination (WLC) of slope gradient, soil type, soil texture, elevation, land cover and drainage density. The parameters are derived from digital global databases. The accuracy assessment was based on a detailed landslide inventory of a 160-km2 area in Japan, using the receiver-operating characteristic (ROC) plot area under the curve (AUC). The AUC permitted to compare analysis approaches and dierent parameter combinations. The AUC for the WLC model was 0.47, below a random classication. Two approaches improved the model accuracy, using the weights of evidence (WOE) approach raised the accuracy to 0.64, and using a higher resolution DEM raised the accuracy to 0.66. On the other hand, a quantitat- ive approach based on logistic regression (LR) and using the software package Spatial Data Modeller (SDM) produced models with AUC between 0.67 and 0.71. The highest accuracy for a model including lithology, slope gradient, prole curvature, plan curvature and elev- ation. The reason for the higher accuracy of the LR models is that the occurrence of landslides depends on local conditions, expressed by the quantitative relations, while the qualitative weights of the WLC model were developed for a global model using different criteria

SINMAP 2.0 - A Stability Index Approach to Terrain Stability Hazard Mapping, User\u27s Manual

SINMAP 2.0 (Stability Index MAPping) is an ArcGIS 9.0 plug-in that implements the computation and mapping of a slope stability index based upon geographic information, primarily digital elevation data. This report describes the theoretical basis for the calculation of the stability index, describes the implementation, presents several case studies and describes use of the accompanying software. SINMAP has its theoretical basis in the infinite plane slope stability model with wetness (pore pressures) obtained from a topographically based steady state model of hydrology. Digital elevation model (DEM) methods are used to obtain the necessary input information (slope and specific catchment area). Parameters are allowed to be uncertain following uniform distributions between specified limits. These may be adjusted (and calibrated) for geographic ccalibration regionsd based upon soil, vegetation or geologic data. The methodology includes an interactive visual calibration that adjusts parameters while referring to observed landslides. The calibration involves adjustment of parameters so that the stability map ccapturesd a high proportion of observed landslides in regions with low stability index, while minimizing the extent of low stability regions and consequent alienation of terrain to regions where landslides have not been observed. This calibration is done while simultaneously referring to the stability index map, a specific catchment area and slope plot (of landslide and non landslide points) where lines distinguish the zones categorized into the different stability classes and a table giving summary statistics. The current implementation of SINMAP 2.0 is a plug-in to the ArcGIS ArcMap geographic information system (GIS) from Environmental Systems Research Institute, Inc. (ESRI). This utilizes ArcMap for its standard GIS functionality such as the input and organization of data and the presentation and output of results. SINMAP is grid based, requiring ArcGIS version 9.0 or higher

PROBABILITY MODEL FOR ARCHAEOLOGICAL SITE LOCATION, A CASE STUDY ON O‘AHU ISLAND, HAWAI‘I

M.A.M.A. Thesis. University of Hawaiʻi at Mānoa 201

OPTIMIZING STOCHASTIC SUSCEPTIBILITY MODELLING FOR DEBRIS FLOW LANDSLIDES: MODEL EXPORTATION, STATISTICAL TECHNIQUES COMPARISON AND USE OF REMOTE SENSING DERIVED PREDICTORS. APPLICATIONS TO THE 2009 MESSINA EVENT.

Il presente lavoro di ricerca è stato sviluppato al fine di approfondire approcci metodologici nell'ambito della sucscettibilità da frana. In particolare, il tema centrale della ricerca è rappresentato dal tema specifico dell'esportazione spaziale di modelli di suscettibilità nell'area mediterranea. All'interno del topic specifico dell'esportazione di modelli predittivi spaziali sono state approfondite tematiche relative all'utilizzo di differenti algoritmi o di differenti sorgenti, derivate da DEM o da coperture satellitari.The present work has been developed in order to enhance current methodological approaches within the big picture of the landslide susceptibility. In particular, the central topic was the spatial exportation of landslide susceptibility models within the Mediterranean sector. Within the specific subject pertaining to the spatial exportation of predictive models, different algorithms as well as different data sources have been tested. Data sources experiments assessed the integration of DEM- and remotely sensed- derived parameters in order to improve the landslide prediction

Development of national extent terrain attributes (tanz), soil water balance surfaces (swatbal), and environmental surfaces, and their application for spatial modelling of pinus radiata productivity across new zealand

The most widely distributed and commercially important forestry crop in New Zealand is Pinus radiata D. Don. Until recently foresters have focussed on maintaining plantation management systems that are highly productive, while remaining sustainable. However, the new era of reduced carbon emissions and carbon trading means forestry systems are now viewed as potential sinks for the sequestration of carbon. Never before has the need to quantify the productive capacity of New Zealand's plantation forests at the national extent been so great. Furthermore, regions of relatively low productivity may become increasingly desirable because these sites require lower capital outlay. In this research, a series of spatial surfaces potentially useful in the modelling and mapping of forest productivity across the national extent of New Zealand have been developed. Modelled surfaces include 15 primary and four secondary terrain attributes; 13 shortwave radiation surfaces, topographically adjusted (one annual and 12 monthly surfaces); and 39 soil water balance model surfaces (one annual and 12 monthly surfaces for fraction of available root zone water storage, available root zone water storage, and drainage). Terrain attributes were developed using a 25-m floating point DEM and are unique and currently the best comprehensive surfaces for the following reasons. (1) Terrain attributes comprehensively encompass the entire country compared with previous piecemeal and site-specific surfaces. (2) Terrain attributes were modelled using a macro-catchment concept that divides the New Zealand landscape into large, naturally draining catchments to avoid the modelling problems associated with edge effects at catchment boundaries. (3) Upslope contributing areas were calculated by switching between an FD8 algorithm that modelled flow divergence in upland regions above defined stream channels and a D8 algorithm used in low-lying areas where modelling of flow convergence is appropriate. (4) Where appropriate, terrain attributes were corrected for undesirable spurious sinks inherent in the 25-m floating point DEM, while retaining naturally occurring sinks in karst environments, depressional lakes and wetlands. This correction provided a continuous surface that modelled flow either to a sink or continuously across the surface until reaching the sea. The soil water balance model, SWatBal, is a dynamic spatial model that can be updated over time as new and improved data become available. SWatbal calculates the fraction of available root-zone water content, available root-zone water content, and drainage for the P. radiata species at a 100-m resolution throughout New Zealand. SWatBal was applied in this study to derive monthly mean soil water balance values, but the model can easily be adjusted to calculate any spatial extent or period. A further advance of SWatBal is the development of reasoned and allocated virtual (RAV) rainfall data. RAV consist of 365 rainfall surfaces representing the normal rainfall distribution on a monthly basis. The advantage RAV data have over monthly mean rainfall is that rainfall distribution of an actual month is used, making the data realistic, rather than assuming constant rainfall across each day for a month. A shortwave-radiation model was developed for New Zealand at a 25-m cell-size resolution utilising a national extent DEM and a latitude surface. This shortwave radiation model encompassed slope and aspect adequately while simultaneously accounting for the influence of terrain shading. As a model it has simplicity, flexibility, and minimal computation time and storage requirements. A partial least squares (PLS) regression technique was used to develop the surfaces of (i) stem volume mean annual increment at age thirty years for a defined reference regime of 300 stems ha-1 (300 Index), and (ii) mean top height at age twenty (Site Index) using TANZ, SWatBal and other developed and existing New Zealand spatial datasets. Together, (i) and (ii) provided the basis for a spatial model of P. radiata productivity. Initially, the 300 Index and Site Index values were calculated for 1698 permanent sampling plot (PSP) locations. For cross validation purposes, 552 PSP sites were withheld from all modelling procedures. PLS regression was used to model and predict 300 Index and Site Index values using previously developed and some existing datasets including climate, landuse, terrain, and their environmental surfaces. Best models explained 58% and 67 % of the variance for 300 Index and Site Index, respectively. The PLS models were also used to develop quantitative productivity maps across the national extent of New Zealand. In addition, a regression kriging (RK) technique was used, where ordinary kriging (OK) of the PLS model residuals was undertaken to improve model outcomes by summing the PLS and OK surfaces. Cross validation showed that prediction precision increased for both the 300 Index and Site Index RK models. However, only Site Index predictions were considered less biased using the RK technique. Findings from the commonly used and relatively straight forward spatial interpolation technique, inverse distance weighting (IDW), were compared with those derived using the more complex RK, OK, and PLS techniques. Cross validation showed that all techniques performed better than their respective data means. OK, RK, and IDW techniques were similar in prediction precision with the IDW prediction precision best for the 300 Index, and RK best for the Site Index. However, OK predictions showed reduced prediction bias. Having stated that RK, OK, and IDW interpolation techniques provided overall better predictions than PLS, it is emphasised that cross validation locations only occur within currently forested landscapes. Beyond these forested regions PLS regression has an inordinate advantage over OK and IDW prediction techniques by utilising local environmental and landform information. Additionally, there is the potential of prediction improvement through the coupling of the PLS model with its kriged regression residuals. Indeed, the main purpose of producing the 300 Index and Site Index maps was to provide empirically based predictions of regions currently without forests as much as regions with forests through spatial interpolation of existing national extent observed PSP data. Possible drivers of P. radiata productivity across 14 broad LENZ-derived environmental regimes were also assessed. It was found that generally air temperature and water balance variables were the predominate drivers

Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster

We develop a stochastic modeling approach based on spatial point processes of log-Gaussian Cox type for a collection of around 5000 landslide events provoked by a precipitation trigger in Sicily, Italy. Through the embedding into a hierarchical Bayesian estimation framework, we can use the Integrated Nested Laplace Approximation methodology to make inference and obtain the posterior estimates. Several mapping units are useful to partition a given study area in landslide prediction studies. These units hierarchically subdivide the geographic space from the highest grid-based resolution to the stronger morphodynamic-oriented slope units. Here we integrate both mapping units into a single hierarchical model, by treating the landslide triggering locations as a random point pattern. This approach diverges fundamentally from the unanimously used presence-absence structure for areal units since we focus on modeling the expected landslide count jointly within the two mapping units. Predicting this landslide intensity provides more detailed and complete information as compared to the classically used susceptibility mapping approach based on relative probabilities. To illustrate the model's versatility, we compute absolute probability maps of landslide occurrences and check its predictive power over space. While the landslide community typically produces spatial predictive models for landslides only in the sense that covariates are spatially distributed, no actual spatial dependence has been explicitly integrated so far for landslide susceptibility. Our novel approach features a spatial latent effect defined at the slope unit level, allowing us to assess the spatial influence that remains unexplained by the covariates in the model

Towards development of fuzzy spatial datacubes : fundamental concepts with example for multidimensional coastal erosion risk assessment and representation

Les systèmes actuels de base de données géodécisionnels (GeoBI) ne tiennent généralement pas compte de l'incertitude liée à l'imprécision et le flou des objets; ils supposent que les objets ont une sémantique, une géométrie et une temporalité bien définies et précises. Un exemple de cela est la représentation des zones à risque par des polygones avec des limites bien définies. Ces polygones sont créés en utilisant des agrégations d'un ensemble d'unités spatiales définies sur soit des intérêts des organismes responsables ou les divisions de recensement national. Malgré la variation spatio-temporelle des multiples critères impliqués dans l’analyse du risque, chaque polygone a une valeur unique de risque attribué de façon homogène sur l'étendue du territoire. En réalité, la valeur du risque change progressivement d'un polygone à l'autre. Le passage d'une zone à l'autre n'est donc pas bien représenté avec les modèles d’objets bien définis (crisp). Cette thèse propose des concepts fondamentaux pour le développement d'une approche combinant le paradigme GeoBI et le concept flou de considérer la présence de l’incertitude spatiale dans la représentation des zones à risque. En fin de compte, nous supposons cela devrait améliorer l’analyse du risque. Pour ce faire, un cadre conceptuel est développé pour créer un model conceptuel d’une base de donnée multidimensionnelle avec une application pour l’analyse du risque d’érosion côtier. Ensuite, une approche de la représentation des risques fondée sur la logique floue est développée pour traiter l'incertitude spatiale inhérente liée à l'imprécision et le flou des objets. Pour cela, les fonctions d'appartenance floues sont définies en basant sur l’indice de vulnérabilité qui est un composant important du risque. Au lieu de déterminer les limites bien définies entre les zones à risque, l'approche proposée permet une transition en douceur d'une zone à une autre. Les valeurs d'appartenance de plusieurs indicateurs sont ensuite agrégées basées sur la formule des risques et les règles SI-ALORS de la logique floue pour représenter les zones à risque. Ensuite, les éléments clés d'un cube de données spatiales floues sont formalisés en combinant la théorie des ensembles flous et le paradigme de GeoBI. En plus, certains opérateurs d'agrégation spatiale floue sont présentés. En résumé, la principale contribution de cette thèse se réfère de la combinaison de la théorie des ensembles flous et le paradigme de GeoBI. Cela permet l’extraction de connaissances plus compréhensibles et appropriées avec le raisonnement humain à partir de données spatiales et non-spatiales. Pour ce faire, un cadre conceptuel a été proposé sur la base de paradigme GéoBI afin de développer un cube de données spatiale floue dans le system de Spatial Online Analytical Processing (SOLAP) pour évaluer le risque de l'érosion côtière. Cela nécessite d'abord d'élaborer un cadre pour concevoir le modèle conceptuel basé sur les paramètres de risque, d'autre part, de mettre en œuvre l’objet spatial flou dans une base de données spatiales multidimensionnelle, puis l'agrégation des objets spatiaux flous pour envisager à la représentation multi-échelle des zones à risque. Pour valider l'approche proposée, elle est appliquée à la région Perce (Est du Québec, Canada) comme une étude de cas.Current Geospatial Business Intelligence (GeoBI) systems typically do not take into account the uncertainty related to vagueness and fuzziness of objects; they assume that the objects have well-defined and exact semantics, geometry, and temporality. Representation of fuzzy zones by polygons with well-defined boundaries is an example of such approximation. This thesis uses an application in Coastal Erosion Risk Analysis (CERA) to illustrate the problems. CERA polygons are created using aggregations of a set of spatial units defined by either the stakeholders’ interests or national census divisions. Despite spatiotemporal variation of the multiple criteria involved in estimating the extent of coastal erosion risk, each polygon typically has a unique value of risk attributed homogeneously across its spatial extent. In reality, risk value changes gradually within polygons and when going from one polygon to another. Therefore, the transition from one zone to another is not properly represented with crisp object models. The main objective of the present thesis is to develop a new approach combining GeoBI paradigm and fuzzy concept to consider the presence of the spatial uncertainty in the representation of risk zones. Ultimately, we assume this should improve coastal erosion risk assessment. To do so, a comprehensive GeoBI-based conceptual framework is developed with an application for Coastal Erosion Risk Assessment (CERA). Then, a fuzzy-based risk representation approach is developed to handle the inherent spatial uncertainty related to vagueness and fuzziness of objects. Fuzzy membership functions are defined by an expert-based vulnerability index. Instead of determining well-defined boundaries between risk zones, the proposed approach permits a smooth transition from one zone to another. The membership values of multiple indicators (e.g. slop and elevation of region under study, infrastructures, houses, hydrology network and so on) are then aggregated based on risk formula and Fuzzy IF-THEN rules to represent risk zones. Also, the key elements of a fuzzy spatial datacube are formally defined by combining fuzzy set theory and GeoBI paradigm. In this regard, some operators of fuzzy spatial aggregation are also formally defined. The main contribution of this study is combining fuzzy set theory and GeoBI. This makes spatial knowledge discovery more understandable with human reasoning and perception. Hence, an analytical conceptual framework was proposed based on GeoBI paradigm to develop a fuzzy spatial datacube within Spatial Online Analytical Processing (SOLAP) to assess coastal erosion risk. This necessitates developing a framework to design a conceptual model based on risk parameters, implementing fuzzy spatial objects in a spatial multi-dimensional database, and aggregating fuzzy spatial objects to deal with multi-scale representation of risk zones. To validate the proposed approach, it is applied to Perce region (Eastern Quebec, Canada) as a case study