153 research outputs found

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

    Get PDF
    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

    Guidelines for the selection of appropriate remote sensing technologies for landslide detection, monitoring and rapid mapping: the experience of the SafeLand European Project.

    Get PDF
    New earth observation satellites, innovative airborne platforms and sensors, high precision laser scanners, and enhanced ground-based geophysical investigation tools are a few examples of the increasing diversity of remote sensing technologies used in landslide analysis. The use of advanced sensors and analysis methods can help to significantly increase our understanding of potentially hazardous areas and helps to reduce associated risk. However, the choice of the optimal technology, analysis method and observation strategy requires careful considerations of the landslide process in the local and regional context, and the advantages and limitations of each technique. Guidelines for the selection of the most suitable remote sensing technologies according to different landslide types, displacement velocities, observational scales and risk management strategies have been proposed. The guidelines are meant to aid operational decision making, and include information such as spatial resolution and coverage, data and processing costs, and maturity of the method. The guidelines target scientists and end-users in charge of risk management, from the detection to the monitoring and the rapid mapping of landslides. They are illustrated by recent innovative methodologies developed for the creation and updating of landslide inventory maps, for the construction of landslide deformation maps and for the quantification of hazard. The guidelines were compiled with contributions from experts on landslide remote sensing from 13 European institutions coming from 8 different countries. This work is presented within the framework of the SafeLand project funded by the European Commission’s FP7 Programme.JRC.H.7-Climate Risk Managemen

    A ROC analysis-based classification method for landslide susceptibility maps

    Full text link
    [EN] A landslide susceptibility map is a crucial tool for landuse spatial planning and management in mountainous areas. An essential issue in such maps is the determination of susceptibility thresholds. To this end, the map is zoned into a limited number of classes. Adopting one classification system or another will not only affect the map's readability and final appearance, but most importantly, it may affect the decision-making tasks required for effective land management. The present study compares and evaluates the reliability of some of the most commonly used classification methods, applied to a susceptibility map produced for the area of La Marina (Alicante, Spain). A new classification method based on ROC analysis is proposed, which extracts all the useful information from the initial dataset (terrain characteristics and landslide inventory) and includes, for the first time, the concept of misclassification costs. This process yields a more objective differentiation of susceptibility levels that relies less on the intrinsic structure of the terrain characteristics. The results reveal a considerable difference between the classification methods used to define the most susceptible zones (in over 20% of the surface) and highlight the need to establish a standard method for producing classified susceptibility maps. The method proposed in the study is particularly notable for its consistency, stability and homogeneity, and may mark the starting point for consensus on a generalisable classification method.Cantarino-Martí, I.; Carrión Carmona, MÁ.; Goerlich-Gisbert, F.; Martínez Ibáñez, V. (2018). A ROC analysis-based classification method for landslide susceptibility maps. Landslides. 1-18. doi:10.1007/s10346-018-1063-4S118Armstrong MP, Xiao N, Bennett DA (2003) Using genetic algorithms to create multicriteria class intervals for choropleth maps. Ann Assoc Am Geogr 93(3):595–623. https://doi.org/10.1111/1467-8306.9303005Atkinson P, Massari R (1998) Generalised linear modelling of susceptibility to landsliding in the central Apennines, Italy. Comput Geosci 24(4):373–385. https://doi.org/10.1016/S0098-3004(97)00117-9Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65(1–2):15–31. https://doi.org/10.1016/j.geomorph.2004.06.010Baeza C, Lantada N, Amorim S (2016) Statistical and spatial analysis of landslide susceptibility maps with different classification systems. Environ Earth Science 75:1318. https://doi.org/10.1007/s12665-016-6124-1Basofi A, Fariza A, Ahsan AS, Kamal IM (2015) A comparison between natural and head/tail breaks in LSI (landslide susceptibility index) classification for landslide susceptibility mapping: a case study in Ponorogo, East Java, Indonesia. 2015 International Conference on Science in Information Technology, pp 337–342Cantarino I (2013) Elaboración y validación de un modelo jerárquico derivado de SIOSE. Revista de Teledetección 39:5–21Carrara A, Crosta GB, Frattini P (2008) Comparing models of debris-flow susceptibility in the alpine environment. Geomorphology 94(3–4):353–378. https://doi.org/10.1016/j.geomorph.2006.10.033Chacón J, Irigaray C, Fernández T, El Hamdouni R (2006) Engineering geology maps: landslides and geographical information systems. Bull Eng Geol Environ 65(4):341–411Chung CJF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30:451–472COPUT (1998) Lithology, exploitation of industrial rocks and landslide risk in the Valencian Community. Thematic Mapping Series. Department of Public Works of the Valencian Regional GovernmentDrummond C, Holte RC (2006) Cost curves: an improved method for visualizing classifier performance. Mach Learn 65(1):95–130Duman TY, Can T, Gokceoglu C, Nefeslioglu HA, Sonmez H (2006) Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey. Environ Geol 51(2):241–256. https://doi.org/10.1007/s00254-006-0322-1Evans IS (1977) The selection of class intervals. Transactions of the Institute of British Geographers. Contemp Cartograph 2(1):98–124. https://doi.org/10.2307/622195Fleiss JL, Levin B, Paik MC (2003) Statistical methods for rates and proportions, Book Series: Wiley Series in Probability and Statistics. John Wiley & Sons. Print ISBN: 9780471526292. doi: https://doi.org/10.1002/0471445428Foody GM (2004) Thematic map comparison: evaluating the statistical significance of differences in classification accuracy. Photogramm Eng Remote Sens 70(5):627–633Fotheringham AS, Brunsdon C, Charlton M (2000) Quantitative geography: perspectives on spatial data analysis. SAGE Publications, Thousand Oaks 270 ppFrattini P, Crosta G, Carrara A (2010) Techniques for evaluating the performance of landslide susceptibility models. Eng Geol 111(1–4):62–72. https://doi.org/10.1016/j.enggeo.2009.12.004Geisser S (1998) Comparing two tests used for diagnostic or screening processes. Stat Probability Lett 40:113–119Greiner M, Pfeiffer D, Smith RD (2000) Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Prev Vet Med 45:23–41Günther A, Reichenbach P, Malet JP, van den Eeckhaut M, Hervás J, Dashwood C, Guzzetti F (2013) Tier-based approaches for landslide susceptibility assessment in Europe. Landslides 10:529–546. https://doi.org/10.1007/s10346-012-0349-1Günther A, Van Den Eeckhaut M, Malet J-P, Reichenbach P, Hervás J (2014) Climate-physiographically differentiated Pan-European landslide susceptibility assessment using spatial multi-criteria evaluation and transnational landslide information. Geomorphology 224:69–85Gupta RP, Kanungo DP, Arora MK, Sarkar S (2008) Approaches for comparative evaluation of raster GIS-based landslide susceptibility zonation maps. Int J Appl Earth Obs Geoinf 10(3):330–341. https://doi.org/10.1016/j.jag.2008.01.003Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81(1–2):166–184. https://doi.org/10.1016/j.geomorph.2006.04.007Hervás J (2017) El inventario de movimientos de ladera de España ALISSA: Metodología y análisis preliminar. In: Alonso E, Corominas J, Hürlimann M (Eds.), Taludes 2017. Proc. IX Simposio Nacional sobre Taludes y Laderas Inestables, Santander, 27–30 June 2017. CIMNE, Barcelona, pp. 629–639Jaedicke C, Van Den Eeckhaut M, Nadim F et al (2014) Identification of landslide hazard and risk ‘hotspots’ in Europe. Bull Eng Geol Environ 73:325. https://doi.org/10.1007/s10064-013-0541-0Jenks GF (1967) The data model concept in statistical mapping. Int Yearbook Cartograph 7:186–190Jiang B (2013) Head/tail breaks: a new classification scheme for data with a heavy-tailed distribution. Prof Geogr 65(3):482–494. https://doi.org/10.1080/00330124.2012.700499Kiang MY (2003) A comparative assessment of classification methods. Decis Support Syst 35(4):441–454. https://doi.org/10.1016/S0167-9236(02)00110-0Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33(1):159–174Langping L, Hengxing L, Changbao G, Yongshuang Z, Quanwen L, Yuming W (2017) A modified frequency ratio method for landslide susceptibility assessment. Landslides 14:727–741. https://doi.org/10.1007/s10346-016-0771-xLee S (2007) Comparison of landslide susceptibility maps generated through multiple logistic regression for three test areas in Korea. Earth Surf Process Landforms 32:2133–2148. https://doi.org/10.1002/esp.1517Liu C, Frazier P, Kumar L (2007) Comparative assessment of the measures of thematic classification accuracy. Remote Sens Environ 107(4):606–616. https://doi.org/10.1016/j.rse.2006.10.010López-Ratón M, Rodríguez-Álvarez MX, Cadarso-Suárez C, Gude-Sampedro F (2014) Optimal cutpoints: an R package for selecting optimal cutpoints in diagnostic tests. J Stat Softw 61(8):4Malet JP, Puissant A, Mathieu A, Van Den Eeckhaut M, Fressard M (2013) Integrating spatial multi-criteria evaluation and expert knowledge for country-scale landslide susceptibility analysis: application to France. In: Margottini C, Canuti P, Sassa K (eds) Landslide science and practice. Springer, Berlin. https://doi.org/10.1007/978-3-642-31325-7_40McGee S (2002) Simplifying likelihood ratios. J Gen Intern Med 17:647–650Metz C (1978) Basic principles of ROC analysis. Semin Nucl Med VIII(4):183–198Nadim F, Kjekstad O, Peduzzi P, Herold C, Jaedicke C (2006) Global landslide and avalanche hotspots. Landslides 3:159–173. https://doi.org/10.1007/s10346-006-0036-1Ohlmacher G, Davis J (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Eng Geol 69(3–4):331–343. https://doi.org/10.1016/S0013-7952(03)00069-3Powell RL, Matzke N, de Souza C Jr, Clark M, Numata I, Hess LL, Roberts DA (2004) Sources of error accuracy assessment of thematic land-cover maps in the Brazilian Amazon. Remote Sens Environ 90(2):221–234. https://doi.org/10.1016/j.rse.2003.12.007Saaty T (1980) The analytic hierarchy process. McGraw Hill, New YorkSmits PC, Dellepiane SG, Schowengerdt RA (1999) Quality assessment of image classification algorithms for land-cover mapping: a review and proposal for a cost-based approach. Int J Remote Sens 20:1461–1486Stehman SV, Czaplewski RL (1998) Design and analysis of thematic map accuracy assessment: fundamental principles. Remote Sens Environ 64:331–344Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240(4857):1285–1293Van Den Eeckhaut M, Hervás J, Jaedicke C, Malet J-P, Montanarella L, Nadim F (2012) Statistical modelling of Europe-wide landslide susceptibility using limited landslide inventory data. Landslides 8:357–369Varnes DJ (1984) Landslide hazard zonation: a review of principles and practice. Natural hazards. UNESCO, ParisZhu X (2016) GIS for environmental applications. Routledge, Abingdon, p 490Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 39(4):561–57

    Effects of Helicobacter suis γ-glutamyl transpeptidase on lymphocytes: modulation by glutamine and glutathione supplementation and outer membrane vesicles as a putative delivery route of the enzyme

    Get PDF
    Helicobacter (H.) suis colonizes the stomach of the majority of pigs as well as a minority of humans worldwide. Infection causes chronic inflammation in the stomach of the host, however without an effective clearance of the bacteria. Currently, no information is available about possible mechanisms H. suis utilizes to interfere with the host immune response. This study describes the effect on various lymphocytes of the γ-glutamyl transpeptidase (GGT) from H. suis. Compared to whole cell lysate from wild-type H. suis, lysate from a H. suis ggt mutant strain showed a decrease of the capacity to inhibit Jurkat T cell proliferation. Incubation of Jurkat T cells with recombinantly expressed H. suis GGT resulted in an impaired proliferation, and cell death was shown to be involved. A similar but more pronounced inhibitory effect was also seen on primary murine CD4+ T cells, CD8+ T cells, and CD19+ B cells. Supplementation with known GGT substrates was able to modulate the observed effects. Glutamine restored normal proliferation of the cells, whereas supplementation with reduced glutathione strengthened the H. suis GGT-mediated inhibition of proliferation. H. suis GGT treatment abolished secretion of IL-4 and IL-17 by CD4+ T cells, without affecting secretion of IFN-γ. Finally, H. suis outer membrane vesicles (OMV) were identified as a possible delivery route of H. suis GGT to lymphocytes residing in the deeper mucosal layers. Thus far, this study is the first to report that the effects on lymphocytes of this enzyme, not only important for H. suis metabolism but also for that of other Helicobacter species, depend on the degradation of two specific substrates: glutamine and reduced glutatione. This will provide new insights into the pathogenic mechanisms of H. suis infection in particular and infection with gastric helicobacters in general
    corecore