Influência dos Modelos Digitais de Elevação na Susceptibilidade a Escorregamento com Modelo de Regressão Logística
DOI:
https://doi.org/10.11606/rdg.v36i0.150111Keywords:
Statistical Modelling, Landslides, Agriculture Terraces, Douro Demarcated RegionAbstract
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
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
Issue
Section
License
Autores que publicam nesta revista concordam com os seguintes termos:
- Autores mantém os direitos autorais e concedem à revista o direito de primeira publicação, com o trabalho simultaneamente licenciado sob a Licença Creative Commons Attribution BY-NC-SA que permite o compartilhamento do trabalho com reconhecimento da autoria e publicação inicial nesta revista.
- Autores têm autorização para assumir contratos adicionais separadamente, para distribuição não-exclusiva da versão do trabalho publicada nesta revista (ex.: publicar em repositório institucional ou como capítulo de livro), com reconhecimento de autoria e publicação inicial nesta revista. A licença adotada enquadra-se no padrão CC-BY-NC-SA.
- Autores têm permissão e são estimulados a publicar e distribuir seu trabalho online (ex.: em repositórios institucionais ou na sua página pessoal) a qualquer ponto antes ou durante o processo editorial, já que isso pode gerar alterações produtivas, bem como aumentar o impacto e a citação do trabalho publicado (Veja O Efeito do Acesso Livre).