Evaluation artificial neural network method for spatial mapping of species potential habitat (Case study: Rangeland Siah‌bisheh, Mazandaran)

Document Type : Research Paper

Authors

q

Abstract

Prediction of the spatial distribution of Festuca Ovina and Bromus briziformis in Siahbisheh Rangelands using artificial neural network was the purpose of this study. Random classification sampling was done for vegetation in 29 homogenous units. 290 plot 1 m² were established in the area and was recorded percent of canopy cover. 3 soil samples were collected from a depth of 0-30 in any homogenous unit. In this study, 20 Environmental factors (Slope, aspect, elevation, distance from road, distance from river, precipitation, distance from livestock, geology, percent of silt, clay, sand, moisture, carbon, organic matter, ph, EC and N.P.K) were independent variables and species presence data of Festuca Ovina and Bromus briziformis was dependent variable. The information layers of each these factors prepared in Arc GIS and were classified using the frequency of each these factors. The results showed that the most important environmental variables affecting the distribution of the studied species were elevation, soil texture and nutrients. Then 70 and 30 percent of the data were used for training and test network respectively. In this study, artificial neural network structure with the 20 neurons in the input layer and the hidden layer and one neuron in the output layer, values of MSE were calculated for festuca 0.75 and Bromus 0.72. Then zoning maps of plant species were prepared with 4 zones including absence and presence of low, medium, high. Zoning maps were evaluated using ROC curves and Kappa coefficient that accuracy with ROC curves were 97.10, 84.10 and with kappa coefficient were 0.78, 0.66 percent for Festuca ovina, and Bromus briziformis respectively that represents a good evaluation of model.

Keywords

Ahmadi, A., Shahmoradi, A.A., Zare Kia, S., Nateghi, S. 2013. Evaluation Autecological Astragalus effuses rangeland West Azerbaijan province. Journal of Research of the Iranian desert and rangeland 20(1), 181-172. (in Persian).
Akbarzade, M., Shahmoradi, A.A. 2004. Evaluation of some ecological aspects of plant grass Festuca Ovina, Mazandaran province in Rangeland. Third National Conference on rangeland and range management's Articles collection, 19-17 September, Tehran, 368-357. (in Persian).
Akbari, M., Badiee, H., Ranaee, A. 2011. Kharazmi Assessment (algorithm) Artificial Neural Networks in the evaluation of desertification (The case of the southern city of neyshapur). Journal of Range and Watershed Management 64 (3), 256-243. (in Persian).
Ashcroft, M.B., French, K.O., Chisholm, L.A. 2011. An evaluation of environmental factors effecting species distributions. Ecological Modeling 222(3), 531-524.
Bagheri Shabestari, E.S., Sheidai, M., Assadi, M., Amini, T. 2010. Species relationships in Festuca (poaceae) of Iran. Gene Conserve 9 (38), 247-262. (in Persian).
Bedia, J., Busque, J., Gutierrez, J.M. 2011. Predicting plant species distribution across an alpine rangeland in northern Spain. A comparison of probabilistic methods. Applied Vegetation Science1-18.
Bennie, J., Hill, M.O., Baxter, R., Huntley, B. 2006. Influence of slope and aspect on long-term vegetation change in British chalk grassland. Journal of Ecology 94, 355-368.
James, B. 1973. Turf grass: science and culture, Prentice-Hell, Inc. Englewood Cliffs, N.J, USA, 658p.
Chanter, G.R., Blanco, A.M., Lodovichi, M.V., Bandoni, A.J., Sabbatini, M.R., Lopez, R.L., Vigna, R.L., Gigon, R. 2012. Modeling Avena fatua seedling emergence dynamic: An artificial neural network approach. Computers and Electronics in Agriculture 88, 95-102.
Dehghani, A. 1996. Autecology Festuca Ovina in rangeland Golestan National Park. Master's thesis University of Agricultural Sciences and Natural Resources, Gorgan. 232p. (in Persian).
Guisan, A., Theurillat, j. 2000. Equilibrium modeling of alpine plant distribution: how far can we go? Phytocoenologia 30, 353-384.
Ghanbari, F., Shataee, SH., Dehghani, A.A., ayubi, SH. 2009. Evaluation of forest density using terrain analysis and artificial neural network. Journal of Science and Technology Wood and Forest 4, 42-25. (in Persian).
Ghorbani, M.A., Farsadizade Jahangiri, H., Chabokpor, J., Fathi, P. 2012. Water engineering software, publications Norpardazan. 261p. (in Persian).
Gomez, H., Kavzoglu, T. 2005. Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Engineering Geology 78, 11-27.
Grattan, S.R., Grieve, C.M. 1994. Mineral nutrient acquisition and response by plants grown in saline environments. In: Pessarkli M (eds). Handbook of plant and Crop Stress. Marcel Dekker, New York, 203-229.
Irmak, A., Jones, J.W., Batchlor, W.D., Irmak, S., Bootek, K.J., Paz, J.O. 2006. Artificial neural network model as a data analysis tool in precision farming. American Society of Agricultural and Biological Engineers 49(6), 2027−2037.
Hardtle, W., Oheimb, G.V., Westphal, CH. 2006. The effects of light and soil conditions on the species richness of the ground vegetation of deciduous forest in northern Germany. Forest Ecology and Management 182: 327-338.
Hirzel, A., Guisan, A. 2002. Which is Optimal Sampling Strategy for Habitat Suitability Modeling? Ecological Modeling 157, 331-341.
Jafarian, Z., Arzani, H., Jafari, M., Azarnivand, H. 2012. Mapping spatial prediction plant species using logistic regression (Case Stady: rangelands Reineh, Mount Damavand). Natural geography of researches 79, 18-1. (in Persian).
Jori, M.H., Mahdavi, M. 2010. Applications identification of rangeland plants. 434p. Kia, F., Tavili, A., Javadi, A. 2011. The Relationship between the distributions of several species of of grassland with some environmental factors in this area in Golestan Province. Journal of rangeland 5(3), 301-292.
Karimzadeh, A., Jafarian, Z., Ghorbani, J. 2009. Relationship analysis of vegetation with some environmental factors using multivariate analysis (Case Study: rangelands of Semnan Province). Masters of thesis Sari agricultural of sciences and Natural Resources University, 143 p.
Lee, S. Ryu, J.H. Won, J.S., park, H. 2004. Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Eng Geo. 71, 289-302.
Lee, S., Sambath,T. 2006. Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logostic regression models. The journal of Environmental Geology 50: 847-855.
Lee, S. Ryu, J.H. Lee, M., Won, J.S. 2003. Use of artificial neural networks for analysis of the susceptibility to landslide at Boun, korea. Environmental Geology 44, 820-833.
Lee, S. Ryu, J.H. Lee, M. Won, J.S. 2006. The application of artificial neural networks to landslide susceptibility mapping at Jang hung Korea. Mathematical Geology 38 (2), 199-207.
Landis, J.R., Koch, G.C. 1977. The measurement of observer agreement for categorical data. Biometrics 33, 159-174.
Oh H, J., Pradhan, B. 2011. Application of a neuro-fuzzy model to land slid susceptibility mapping for shallow landslide in a tropical hilly area. Computers & Geosciences.
Mirhaji, T., Sanadgol, A.A. 2007. Study the growth degree day's requirement for phonological stages of important range species of Homand. Iranian Journal of Range & desert 13(3), 212-221.
Paradhan, B., Lee, S. 2010. Landslide susceptibility assessment and factor effect analysis: back propagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modeling. Environmental modeling & Software 747-759.
Rezaee Arshad, R., Sayad, Gh. A., Mazlum, M., Shorafa, M., Jafarnezhad, A.R. 2012. Comparison of artificial neural networks and regression for predicting electrical conductivity of saturated soils in Khuzestan province. Journal of Science and Technology of Agriculture and Natural Resources, Water and Soil Sciences. 16 (60) 118-107. (in Persian).
Rahmati, Z., Tarkeshesfahani, M., Pormanafi, S., Vahabi, M.R. 2015. Determine the potential habitats of species coma (Ferula Ovina Boiss) using artificial neural network in the area of Fereydunshahr Esfehan. Ecology of Applications 4(11) 2-41. (in Persian).
Rakee, B., Khamechian, M., Abdolmalaki, P., Giahchi, P. 2007. Application of artificial neural network landslide hazard zonation (Case Study: Area Sefidar Gale in Semnan province). Tehran University Journal of Science 33(1), 57- 64.
Saeedi Razavi, B. 2014. Predicting the Trend of Land Use Changes Using Artificial Neural Network and Markov chain Model (Case Study: Kermanshah City). Research Journal of Environmental and Earth Sciences 6(4), 215-226.
Sefinian, A., Mohamadi tofighi, A., Khodakarami, L., Amiri, F. 2011. Land use mapping using artificial neural network (case study: watershed Kabudarahang, Rosen and Khvnyn- Talkhab in Hamedan province). Journal of Remote Sensing and GIS in Natural Resource Sciences 2 (1), 13-1.(in Persian).
Safi, Y., Bouroumi, A. 2013. Prediction of forest fires using artificial neural networks. Applied Mathematical Sciences 7(6), 271 – 286.
Sharpley, A.N., Meisinger, J.J., Power, J.F., Suarez, D.L. 1992. Root extraction of nutrients associated with long-term soil managerment. In: stewart, B.(ed), Advances in Soil Science 19,151-217.
Shokri, M., Bahmanyar, M.A., Tatian, M.R. 2003. Ecological Study of Vegetation Rangeland Hezar Jarib Behshahr. Iranian Journal of Natural Resources 56, 142-131.
Silva, D.M., Batalha, A.M. 2008. Soil-vegetation relationships in cerrados under different fire frequencies. Plant Soil 311, 87-96.
Yilmaz, I. 2009. Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison (A case study from Kat Landslides (Tokat-Turkey). Computers & Geosciences 35, 1125-1138.
Zarechahoki, M.A., Zarearnabi, M., Zarechahiki, A., Khalasiahvazi, L. 2010. The use of spatial statistical methods in predictive models of habitat for plant species. Journal of dry canvas. 1(1), 23-13. (in Persian).
Zhang, J., Zhao, R., Zhou, H. 2006. Interrelation between plant communities and environmental factors of wetlands and surrounding lands in mid and lower reaches of Tarim River. J. Appl. Ecol. 17(6), 995-60.
Zhu, C., Wang. X. 2009. Landslide susceptibility mapping: A comparison of information and weights-of evidence methods in Three Gorges Area. International Conference on Environmental Science and Information Application Technology.