Revista Chapingo Serie Ciencias Forestales y del Ambiente
Modeling of landslide sensitive areas using GIS in semi-arid forests and evaluation in terms of forest rehabilitation
ISSNe: 2007-4018   |   ISSN: 2007-3828
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Keywords

Forestry
forest activities
susceptibility map
fuzzy inference system
modified-analytical hierarchy process

How to Cite

Buğday, E., & Barış Özel, H. (2020). Modeling of landslide sensitive areas using GIS in semi-arid forests and evaluation in terms of forest rehabilitation. Revista Chapingo Serie Ciencias Forestales Y Del Ambiente, 26(2), 241–255. https://doi.org/10.5154/r.rchscfa.2019.07.054

##article.highlights##

  • Fuzzy inference system and modified analytical hierarchy process approaches were evaluated.
  • The estimation capability of these model approaches is sufficient and internationally accepted.
  • It can be used as an effective tool in the decision-making process in forest management.
  • These modeling approaches can also be developed and enriched for landslide-sensitive forest lands.

Abstract

Introduction: In order to increase, protect, and sustain forest assets, it is important to determine the factors that affect forestry activities and minimize their impact. In this study, the landslide factor in forestry applications was tackled. The negative effect of unpredictable factors of forestry activities (road construction, harvesting, afforestation, etc.) can be reduced by calculating and modeling the landslide susceptibility ratios of degraded forests.
Objective: To demonstrate the applicability of a landslide susceptibility map for supporting decision makers in the assessment of semi-arid and landslide-sensitive forestlands in forestry activities and rehabilitation works.
Materials and method: Six models were introduced by using the fuzzy inference system (FIS) and modified analytical hierarchy process (M-AHP) approaches. A combination of elevation, slope (degree), distance to faults, lithology, aspect, and plan curvature was used in the models.
Results and discussion: The most successful models under the FIS and M-AHP approaches were FIS Model 3, and M-AHP Model 1, with areas under the curve (AUC) of 80.2 %, and 78.1 %, respectively. Using precision forestry by making decisions based on the area’s landslide susceptibility in the management and planning stage (e.g., construction of forest infrastructure facilities, afforestation, and forest harvesting and rehabilitation), will increase the success of forestry activities.
Conclusion: It is very important to determine the landslide areas in advance and reliably for effective execution of forestry practices in landslide sensitive forestlands, in order to increase the success of forestry activities in accordance with the sustainable forest management approach.

https://doi.org/10.5154/r.rchscfa.2019.07.054
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References

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

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