Influência dos Modelos Digitais de Elevação na Susceptibilidade a Escorregamento com Modelo de Regressão Logística

Authors

  • Ana Oliveira University of Porto. Faculty of Arts
  • Joana Fernandes University of Porto. Faculty of Arts
  • Carlos Bateira Riskam, CEG, IGOT, ULisboa/FLUP, UP
  • Ana Faria University of Porto. Faculty of Arts
  • José Gonçalves University of Porto

DOI:

https://doi.org/10.11606/rdg.v36i0.150111

Keywords:

Statistical Modelling, Landslides, Agriculture Terraces, Douro Demarcated Region

Abstract

This paper focuses on the influence of Digital Elevation Models on the landslides susceptibility assessment in agricultural terraces, using Logistic Regression statistical model. This study was performed in a watershed located at Carvalhas Estate in Douro Valley, using an inventory of 109 landslides. To analyse the influence of the digital elevation model (DEM) resolution we used three DEMs, (A), (B) and (C). The DEMs (A) and (B) were directly obtained by processing aerial images and extracting different resolutions, 1 and 5 meters, respectively. The DEM (C), with 5m resolution, was processed with Topo to Raster interpolation method, using as input data contour lines of 10 m interval, elevation points and hydrography. The Logistic Regression was performed using two models which are distinguished by the independent variables alteration. At model 1 was used the slope, curvature, raiser slope, riser height, contributing areas and topographic wetness index. In scenario 2 we decide remove the independent variables related with the terrace geometry, riser slope and riser height. The results seems to indicate that there is no significant influence of different resolutions of Digital Elevation Models in susceptibility modelling at this small scale and using statistical methods. The independent variables riser slope and riser height provide information of the terraces geometry and the construction techniques that enter the modelling process with more detailed information.

Downloads

Download data is not yet available.

References

Ayalew, 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), 15-31. DOI: https://doi.org/10.1016/j.geomorph.2004.06.010

Carrara, A. (1983). Multivariate models for landslide hazard evaluation. Mathematical geology, 15(3), 403-426.

Carrara, A., Cardinali, M., Detti, R., Guzzetti, F., Pasqui, V., & Reichenbach, P. (1991). GIS techniques and statistical models in evaluating landslide hazard. Earth surface processes and landforms, 16(5), 427-445. DOI: 10.1002/esp.3290160505

Claessens, L., Heuvelink, G. B. M., Schoorl, J. M., & Veldkamp, A. (2005). DEM resolution effects on shallow landslide hazard and soil redistribution modelling. Earth Surface Processes and Landforms, 30(4), pp. 461-477. DOI: 10.1002/esp.1155

Costanzo, D., Chacón, J., Conoscenti, C., Irigaray, C., & Rotigliano, E. (2014). Forward logistic regression for earth-flow landslide susceptibility assessment in the Platani river basin (southern Sicily, Italy). Landslides, 11(4), pp. 639-653. DOI: 10.1007/s10346-013-0415-3

Esteves, A. F. M. (2006). As Rochas Metamórficas na Região de Viseu.

Fawcett, T. (2006) An Introduction to ROC anlysis. Pattern Recognition Letters. 27 (8), pp. 861-874.

Fernandes, J.; Bateira, C.; Soares, L.; Faria, A.; Oliveira, A; Hermenegildo, C.; Moura, C.; Gonçalves, J. (2017). SIMWE model application on susceptbility anlysis to bank gully erosion in Alto Douro Wine Region agriculture terraces. Catena, Vol. 153, pp. 39-49. DOI: https://doi.org/10.1016/j.catena.2017.01.034

Folk, R. L. (1954). The distinction between grain size and mineral composition in sedimentary-rock nomenclature. Journal of Geology. ISSN 0022-1376. Vol. 62, n.º 4, 344 p. DOI: 10.1086/626171

Guns, M., & Vanacker, V. (2012). Logistic regression applied to natural hazards: rare event logistic regression with replications. Natural Hazards and Earth System Sciences, 12(6), 1937-1947. DOI:10.5194/nhess-12-1937-2012

Guzzetti, F., Carrara, A., Cardinali, M., & Reichenbach, P. (1999). Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology, 31(1–4), 181-216. doi:http://dx.doi.org/10.1016/S0169-555X(99)00078-1

Hutchinson, M. F. (1989). A new procedure for gridding elevation and stream line data with automatic removal of spurious pits. journal of Hydrology, 106(3-4), 211-232.

Jun, L., & Cheng-hu, Z. (2003). Appropriate Grid Size for Terrain Based Landslide Risk Assessment in Lantau Island,Hong Kong.

Landau, S., & Everitt, B. S. (2004). A Handbook of Statistical Analyses Using SPSS: Taylor & Francis

Lee, S., Choi, J., & Woo, I. (2004). The effect of spatial resolution on the accuracy of landslide susceptibility mapping: a case study in Boun, Korea. Geosciences Journal, 8(1), 51-60. doi:10.1007/BF02910278

Mora, O. E., Lenzano, M. G., Toth, C. K., & Grejner-Brzezinska, D. A. (2014). Analyzing the Effects of Spatial Resolution for Small Landslide Susceptibility and Hazard Mapping. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(1), 293. DOI:10.5194/isprsarchives-XL-1-293-2014

Ribeiro, A. (1979). Introduction à la géologie générale du Portugal: Serviços geológicos de Portugal.

Sangchini, E. K., Nowjavan, M. R., & Arami, A. (2015). Landslide susceptibility mapping using logistic statistical regression in Babaheydar Watershed, Chaharmahal Va Bakhtiari Province, Iran. Journal of the Faculty of Forestry Istanbul University| İstanbul Üniversitesi Orman Fakültesi Dergisi, 65(1), 30-40. DOI: 10.17099/jffiu.52751

Sangchini, E. K., Nowjavan, M. R., & Arami, A. (2015). Landslide susceptibility mapping using logistic statistical regression in Babaheydar Watershed, Chaharmahal Va Bakhtiari Province, Iran. Journal of the Faculty of Forestry Istanbul University| İstanbul Üniversitesi Orman Fakültesi Dergisi, 65(1), 30-40.

Stefanescu, E.R., Bursik, M., Patra, A.K. (2012). Effect of digital elevation model on Mohr-Coulomb geophysical flow model output. Natural Hazards, 62, pp. 635-656. DOI:10.1007/s11069-012-0103-y

Tarboton, D. G. (1997). A new method for the determination of flow directions and upslope areas in grid digital elevation models. Water Resources Research, 33(2), 309-319

Tian, Y., XiaO, C., Liu, Y., & Wu, L. (2008). Effects of raster resolution on landslide susceptibility mapping: A case study of Shenzhen. Science in China Series E: Technological Sciences, 51(2), 188-198. doi:10.1007/s11431-008-6009-y

World reference base for soil resources. (2006). International soil classification system for naming soils and creating legends for soil maps. World soil resources reports no. 106, FAO, Rome.

Zêzere, J.; Pereira, S.; Melo, R.; Oliveira, S.; Garcia, R. (2017) Mapping landslide susceptibility using data-driven methods. Sci. Total Environ, 589, pp. 250–267. DOI: https://doi.org/10.1016/j.scitotenv.2017.02.188

Zhang, W., & Montgomery, D. R. (1994). Digital elevation model grid size, landscape representation, and hydrologic simulations. Water Resources Research, 30(4), 1019-1028. DOI:10.1029/93WR03553

Downloads

Published

2018-12-20

Issue

Section

Artigos

How to Cite

Oliveira, A., Fernandes, J., Bateira, C., Faria, A., & Gonçalves, J. (2018). Influência dos Modelos Digitais de Elevação na Susceptibilidade a Escorregamento com Modelo de Regressão Logística. Revista Do Departamento De Geografia, 36, 33-47. https://doi.org/10.11606/rdg.v36i0.150111