Palabras clave
actividades forestales
mapa de susceptibilidad
sistema de inferencia difusa
proceso de jerarquía analítica modificada
Cómo citar
##article.highlights##
- Se evaluaron los enfoques del sistema de inferencia difusa y del proceso de jerarquía analítica modificado.
- Estos enfoques de modelación se evaluaron en tierras forestales susceptibles a deslizamientos de tierra.
- La capacidad de estimación de estos enfoques de modelación es suficiente e internacionalmente aceptada.
- La modelación puede utilizarse como una herramienta eficaz dentro del proceso de toma de decisiones en el manejo forestal.
Resumen
Introducción: Para aumentar, proteger y mantener los recursos forestales, es importante determinar los factores que afectan las actividades forestales y minimizar su impacto. En este estudio se abordó el factor de deslizamientos de tierra en aplicaciones forestales. El efecto negativo de factores impredecibles de las actividades silvícolas (construcción de carreteras, cosecha, forestación, etc.) puede reducirse calculando y modelando las relaciones de susceptibilidad a deslizamientos de tierra de bosques degradados.
Objetivo: Demostrar la aplicabilidad de un mapa de susceptibilidad de deslizamientos de tierra para apoyar a los tomadores de decisiones en la evaluación de bosques semiáridos, en actividades forestales y trabajos de rehabilitación.
Materiales y métodos: Se introdujeron seis modelos utilizando los enfoques del sistema de inferencia difusa (FIS) y del proceso de jerarquía analítica modificada (M-AHP). Se utilizó una combinación de elevación, pendiente (grado), distancia a las fallas, litología, aspecto y curvatura del plan en los modelos.
Resultados y discusión: Los modelos más exitosos bajo los enfoques FIS y M-AHP fueron el Modelo 3 del FIS y el Modelo 1 del M-AHP, con áreas bajo la curva (AUC) de 80.2 % y 78.1 %, respectivamente. El uso de la silvicultura con precisión mediante la toma de decisiones basadas en la susceptibilidad a los deslizamientos de tierra, en la etapa de gestión y planificación (por ejemplo, construcción de instalaciones de infraestructura forestal, forestación y aprovechamiento y rehabilitación de bosques), aumentará el éxito de las actividades silvícolas.
Conclusión: Es muy importante determinar las áreas de deslizamiento de tierra de manera anticipada y confiable para la ejecución efectiva de prácticas silvícolas en tierras forestales susceptibles, con el fin de aumentar el éxito de las actividades silvícolas de acuerdo con el enfoque de gestión forestal sostenible.
Citas
Akgun, A., Dag, S., & Bulut, F. (2008). Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Environmental Geology, 54(6), 1127‒1143. doi: https://doi.org/10.1007/s00254-007-0882-8
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(1-2), 15‒31. doi: https://doi.org/10.1016/j.geomorph.2004.06. 010
Bai, S. B., Wang, J., Lü, G. N., Zhou, P. G., Hou, S. S., & Xu, S. N. (2010). GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. Geomorphology, 115(1-2), 23‒31. doi: https://doi.org/10.1016/j.geomorph.2009.09.025
Banuelas, R., & Antony, J. (2004). Modified analytic hierarchy process to incorporate uncertainty and managerial aspects. International Journal of Production Research, 42(18), 3851‒3872. doi: https://doi.org/10.1080/00207540410001699183
Behling, R., Roessner, S., Kaufmann, H., & Kleinschmit, B. (2014). Automated spatiotemporal landslide mapping over large areas using rapideye time series data. Remote Sensing, 6(9), 8026‒8055. doi: https://doi.org/10.3390/rs6098026
Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management Science, 17(4), B141‒B164. Retrieved from https://pubsonline.informs.org/doi/abs/10.1287/mnsc.17.4.b141
Bizikova, L., & Krcmar, E. (2015). Integrated scenario planning and multi-criteria decision analysis framework with application to forest planning. Open Journal of Forestry, 5(2), 139‒153. doi: https://doi.org/10.4236/ojf.2015.52014
Buğday, E. (2018). Application of artificial neural network system based on anfis using GIS for predicting forest road network suitability mapping. Fresenius Environmental Bulletin, 27(3), 1656‒1668. Retrieved from researchgate.net/publication/323749583_APPLICATION_OF_ARTIFICIAL_NEURAL_NETWORK_SYSTEM_BASED_ON_ANFIS_USING_GIS_FOR_PREDICTING_FOREST_ROAD_NETWORK_SUITABILITY_MAPPING
Buğday, E. (2019). Landslide susceptibility mapping using different modeling approaches in forested areas (Sample of Çankırı-Yapraklı). European Journal of Forest Engineering, 5(2), 61‒67. doi: https://doi.org/10.33904/ejfe.582276
Chang, S. S., & Zadeh, L. A. (1996). On fuzzy mapping and control. In G. J. Klir & B. Yuan (Eds.), Fuzzy sets, fuzzy logic, and fuzzy systems (pp. 180‒184). USA: World Scientific. doi: https://doi.org/10.1142/9789814261302_0012
Chen, W., Pourghasemi, H. R., & Naghibi, S. A. (2018). A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China. Bulletin of Engineering Geology and the Environment, 77, 647–664. doi: https://doi.org/10.1007/s10064-017-1010-y
Chen, W., Pourghasemi, H. R., & Zhao, Z. (2017). A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping. Geocarto International, 32(4), 367‒385. doi: https://doi.org/10.1080/10106049.2016.1140824
Dahal, R. K., Hasegawa, S., Nonomura, A., Yamanaka, M., Masuda, T., & Nishino, K. (2008). GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. Environmental Geology, 54(2), 311‒324. doi: https://doi.org/10.1007/s00254-007-0818-3
Daoyin, W., & Yaoxiang, L. (2007). Modes and methods of forest assets evaluation for the timber forests. Forest Engineering, 4, 29.
DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988). Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics, 44(3), 837‒845. doi: https://doi.org/10.2307/2531595
Devkota, K. C., Regmi, A. D., Pourghasemi, H. R., Yoshida, K., Pradhan, B., Ryu, I. C., & Althuwaynee, O. F. (2013). Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya. Natural Hazards, 65(1), 135‒165. doi: https://doi.org/10.1007/s11069-012-0347-6
Duman, T. Y., Çan, T., & Emre, Ö. (2011). 1/1.500.000 scale landslide inventory map of Turkey. General Directorate of Mineral Research and Exploration special publications series-27, Ankara, Turkey. Retrieved from http://www.mta.gov.tr/eng/maps/landslide-1500000
Eskandari, M., Homaee, M., & Falamaki, A. (2016). Landfill site selection for municipal solid wastes in mountainous areas with landslide susceptibility. Environmental Science and Pollution Research, 23(12), 12423‒12434. doi: https://doi.org/10.1007/ s11356-016-6459-x
Fang, J., Shilong, P., Zhou, L., He, J., Wei, F., Myneni, R. B., Tucker, C. J., & Tan, K. (2005). Precipitation patterns alter growth of temperate vegetation. Geophysical Research Letters, 32(21). doi: https://doi.org/10.1029/2005GL024231
Food and Agriculture Organization of the United Nations (FAO). (2018). The state of the world’s forests 2018 - Forest pathways to sustainable development. Rome, Italy: Author. Retrieved from http://www.fao.org/3/a-i9535en.pdf
Feizizadeh, B., Blaschke, T., & Nazmfar, H. (2014). GIS-based ordered weighted averaging and Dempster–Shafer methods for landslide susceptibility mapping in the Urmia Lake Basin, Iran. International Journal of Digital Earth, 7(8), 688‒708. doi: https://doi.org/10.1080/17538947.2012.749950
General Directorate of Forestry (GDF). (2015). Türkiye’de Orman Varlığı 2015. Retrieved from https://www.ogm.gov.tr/ekutuphane/Yayinlar/T%C3%BCrkiye%20Orman%20Varl%C4%B1%C4%9F%C4%B1-2016-2017.pdf
General Directorate of Forestry (GDF). (2017). 2017 Yili Idare Faaliyet Raporu. Retrieved from https://www.ogm.gov.tr/ekutuphane/FaaliyetRaporu/Orman%20Genel%20M%C3%BCd%C3%BCrl%C3%BC%C4%9F%C3%BC%202017%20Y%C4%B1l%C4%B1%20Faaliyet%20Raporu.pdf
Gökceoglu, C., & Aksoy, H. (1996). Landslide susceptibility mapping of the slopes in the residual soils of the Mengen region (Turkey) by deterministic stability analyses and image processing techniques. Engineering Geology, 44(1-4), 147‒161. doi: https://doi.org/10.1016/S0013-7952(97)81260-4
Jacobs, L., Dewitte, O., Poesen, J., Sekajugo, J., Nobile, A., Rossi, M., & Kervyn, M. (2018). Field-based landslide susceptibility assessment in a data-scarce environment: the populated areas of the Rwenzori Mountains. Natural Hazards and Earth System Sciences, 18(1), 105‒124. doi: https://doi.org/10.3929/ethz-b-000234015
Jiménez-Perálvarez, J. D., Irigaray, C., El Hamdouni, R., & Chacón, J. (2011). Landslide-susceptibility mapping in a semi-arid mountain environment: an example from the southern slopes of Sierra Nevada (Granada, Spain). Bulletin of Engineering Geology and the Environment, 70(2), 265‒277. doi: https://doi.org/10.1007/s10064-010-0332-9
Keller, M., Asner, G. P., Silva, N., & Palace, M. (2004). Sustainability of selective logging of upland forest in the Brazilian Amazon carbon budgets and remote sensıng as tools for evaluatıng loggıng effects. In D. J. Zarin, J. R. R. Alavalapati, F. E. Putz, & M. Schmink (Eds.), Working forests in the Neotropics: Conservation through sustainable management? (pp. 41‒63). USA: Columbia University Press. doi: https://doi.org/10.7312/zari12906
Kornejady, A., Pourghasemi, H. R., & Afzali, S. F. (2019). Presentation of RFFR new ensemble model for landslide susceptibility assessment in Iran. In S. Pradhan, V. Vishal, T. Singh (Eds.), Landslides: Theory, practice and modelling (vol. 50, pp. 123‒143). Springer, Cham. doi: https://doi.org/10. 1007/978-3-319-77377-3_7
Lee, S., & Sambath, T. (2006). Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environmental Geology, 50(6), 847‒855. doi: https://doi.org/10.1007/s00254-006-0256-7
Lozano, Y. M., Hortal, S., Armas, C., & Pugnaire, F. I. (2014). Interactions among soil, plants, and microorganisms drive secondary succession in a dry environment. Soil Biology and Biochemistry, 78, 298‒306. doi: https://doi.org/10.1016/j.soilbio.2014.08.007
Meng, Q. K., Miao, F., Zhen, J., Huang, Y., Wang, X. Y., & Peng, Y. (2016). Impact of earthquake-induced landslide on the habitat suitability of giant panda in Wolong, China. Journal of Mountain Science, 13(10), 1789‒1805. doi: https://doi.org/10.1007/s11629-015-3734-0
Nefeslioglu, H. A., Duman, T. Y., & Durmaz, S. (2008). Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey). Geomorphology, 94(3-4), 401‒418. doi: https://doi.org/10.1016/j.geomorph.2006.10.036
Niu, Q., Dang, X., Li, Y., Zhang, Y., Lu, X., & Gao, W. (2018). Suitability analysis for topographic factors in loess landslide research: a case study of Gangu County, China. Environmental Earth Sciences, 77(7), 294. doi: https://doi.org/10.1007/s12665-018-7462-y
Oh, H. J., & Pradhan, B. (2011). Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Computers & Geosciences, 37(9), 1264‒1276. doi: https://doi.org/10.1016/j.cageo.2010.10.012
Oliveira, S. C., Zêzere, J. L., Guillard-Gonçalves, C., Garcia, R. A., & Pereira, S. (2017). Integration of landslide susceptibility maps for land use planning and civil protection emergency management. In K. Sassa, M. Mikoš, & Y. Yin (Eds.), WLF 2017: Advancing culture of living with landslides (pp. 543‒553). Springer, Cham. doi: https://doi.org/10.1007/978-3-319-59469-9_49
Park, H. S., & Sohn, B. J. (2010). Recent trends in changes of vegetation over East Asia coupled with temperature and rainfall variations. Journal of Geophysical Research: Atmospheres, 115 (D14). doi: https://doi.org/10.1029/2009JD012752
Pourghasemi, H. R., & Kerle, N. (2016). Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran. Environmental Earth Sciences, 75(3), 185. doi: https://doi.org/10.1007/s12665-015-4950-1
Pourghasemi, H. R., & Rossi, M. (2017). Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods. Theoretical and Applied Climatology, 130(1-2), 609‒633. doi: https://doi.org/10.1007/s00704-016-1919-2
Pourghasemi, H. R., Moradi, H. R., & Aghda, S. F. (2013). Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Natural Hazards, 69(1), 749‒779. doi: https://doi.org/10.1007/s11069-013-0728-5
Pourghasemi, H. R., Pradhan, B., & Gokceoglu, C. (2012). Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural Hazards, 63(2), 965‒996. doi: https://doi.org/10.1007/ s11069-012-0217-2
Raum, S. (2017). The ecosystem approach, ecosystem services and established forestry policy approaches in the United Kingdom. Land Use Policy, 64, 282‒291. doi: https://doi.org/10.1016/j.landusepol.2017.01.030
Sahin, E. K., Colkesen, I., & Kavzoglu, T. (2018). A comparative assessment of canonical correlation forest, random forest, rotation forest and logistic regression methods for landslide susceptibility mapping. Geocarto International, 35(4) 1‒23. doi: https://doi.org/10.1080/10106049.2018.1516248
Sezer, E. A., Pradhan, B., & Gokceoglu, C. (2011). Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Systems with Applications, 38(7), 8208‒8219. doi: https://doi.org/10.1016/j.eswa.2010.12.167
Sirén, M., Ala-Ilomäki, J., Mäkinen, H., Lamminen, S., & Mikkola, T. (2013). Harvesting damage caused by thinning of Norway spruce in unfrozen soil. International Journal of Forest Engineering, 24(1), 60‒75. doi: https://doi.org/10.1080/19132220.2013. 792155
Song, X. P., Hansen, M. C., Stehman, S. V., Potapov, P. V., Tyukavina, A., Vermote, E. F., & Townshend, J. R. (2018). Global land change from 1982 to 2016. Nature, 560, 639–643. doi: https://doi.org/10.1038/s41586-018-0411-9
Soriano, M., Kainer, K. A., Staudhammer, C. L., & Soriano, E. (2012). Implementing multiple forest management in Brazil nut-rich community forests: Effects of logging on natural regeneration and forest disturbance. Forest Ecology and Management, 268, 92‒102. doi: https://doi.org/10.1016/j.foreco.2011.05.010
Tang, L. L., Cai, X. B., Gong, W. S., Lu, J. Z., Chen, X. L., Lei, Q., & Yu, G. L. (2018). Increased vegetation greenness aggravates water conflicts during lasting and intensifying drought in the poyang lake watershed, China. Forests, 9(1), 24. doi: https://doi.org/10.3390/f9010024
Türkeş, M. (2012). A detailed analysis of the drought, desertification and the United Nations Convention to Combat Desertification. Journal of Marmara European Researches, 20(1), 7‒55. Retrieved from https://www.academia.edu/9689002/ Marmara_Avrupa_Ara%C5%9Ft%C4%B1rmalar%C4%B1_Dergisi_%C3%87evre_%C3%96zel_Say%C4%B1s%C4%B1
Vallauri, D., Aronson, J., Dudley, N., & Vallejo, R. (2005). Monitoring and evaluating forest restoration success. In S. Mansourian, D. Vallauri, & N. Dudley (Eds.), Forest Restoration in Landscapes (pp. 150‒158). New York, USA: Springer.
Wilson, J. S., & Oliver, C. D. (2000). Stability and density management in Douglas-fir plantations. Canadian Journal of Forest Research, 30(6), 910‒920. doi: https://doi.org/10.1139/x00-027
Yalcin, A. (2008). GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. CATENA, 72(1), 1‒12. doi: https://doi.org/10.1016/j.catena. 2007.01.003
Yalcin, A., Reis, S., Aydinoglu, A. C., & Yomralioglu, T. (2011). A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. CATENA, 85(3), 274‒287. doi: https://doi.org/10.1016/j.catena.2011.01.014
Yang, Y., Fang, J., Ma, W., & Wang, W. (2008). Relationship between variability in aboveground net primary production and precipitation in global grasslands. Geophysical Research Letters, 35(23). doi: https://doi.org/10.1029/2008GL035408
Yesilnacar, E., & Topal, T. (2005). Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Engineering Geology, 79(3-4), 251‒266. doi: https://doi.org/10.1016/j.enggeo.2005.02.002
Yilmaz, E., & Cicek, İ. (2018). Detailed Köppen-Geiger climate regions of Turkey Türkiye’nin detaylandırılmış Köppen-Geiger iklim bölgeleri. Journal of Human Sciences, 15(1), 225‒242. doi:10.14687/jhs.v15i1.5040
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
Derechos de autor 2020 Revista Chapingo Serie Ciencias Forestales y del Ambiente