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Rangelands production modeling using an artificial neural network (ANN) and geographic information system (GIS) in Baladeh rangelands, North Iran | ||
| Caspian Journal of Environmental Sciences | ||
| دوره 18، شماره 3، مهر 2020، صفحه 277-290 اصل مقاله (1.17 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22124/cjes.2020.4139 | ||
| نویسندگان | ||
| Ghasem Ali Dianati Tilaki1؛ Maryam Ahmadi Jolandan1؛ Vahid Gholami* 2 | ||
| 1Department of Range Management, Faculty of Natural Resources, Tarbiat Modares University, Noor, Iran | ||
| 2Department of Range and Watershed Management and Dept. of Water Eng. and Environment, Faculty of Natural Resources, University of Guilan, Sowmeh Sara, Iran | ||
| چکیده | ||
| Rangelands production measurement is time-consuming and expensive. Therefore, models are often employed to simulate rangelands conditions as a supplement. Artificial neural network (ANN) is widely used for modeling in environmental studies, yet it cannot preset its results in the form of a map or geo-referenced data. We used ANN to estimate the spatial distribution of rangelands production, then a geographic information system (GIS) was applied as a pre-processing and post-processing framework in rangelands production modeling. The ANN was trained (Rsqr = 0.95, MSE = 0.02) and tested using data from the Baladeh rangelands located in the northern part of Iran. Rangelands production was simulated using a multi-layer perceptron (MLP) network. We estimated rangelands production (using many plots and field studies) as the network output, along with the influencing factors in the production (vegetation, climatic, topographic, edaphic and human factors) as the inputs. After modeling and model optimizing in ANN, the model test was performed (Rsqr=0.8, MSE=0.3). Furthermore, the studied area was divided with the pixels 100×100 m (raster format) in the GIS medium. Then, the digital layers of the network inputs were combined and a raster layer was prepared including the network inputs values and geographic coordinate. The values of pixels (network inputs) were imported in ANN (NeuroSolutions software). Rangelands production was simulated using the validated optimum network in the sites without production measurements. In the next step, the results of ANN simulation were imported in the GIS medium, then rangelands production map was prepared based on the estimated results of ANN. The results indicated that integrating ANN and GIS exhibits high accuracy and performance in rangelands production estimation. Hence, the prepared rangelands production map can be used for planning and managing the rangelands. | ||
| کلیدواژه‌ها | ||
| Production measurement؛ MLP network؛ Rangelands production map؛ Iran | ||
| مراجع | ||
|
Aeinebeygi, S &Khaleghi, MR 2016, An assessment of biennial enclosure effects on range production, condition and trend (Case study: Taftazan rangeland, Shirvan). International Journal of Forest, Soil and Erosion, 6: 33-40.
Alshehri, F, Sultan, M, Karki, S, Alwagdani, E, Alsefry, S, Alharbi, H, Sahour, H & Sturchio, N 2020. Mapping the distribution of shallow groundwater occurrences using Remote Sensing-based statistical modeling over southwest Saudi Arabia. Remote Sensing, 12(9), p.1361.
Amiri, F, Bin, AR & Shariff, M 2011, An approach for analysis of integrated components on available forage in semi-arid rangelands of Iran. World Applied Sciences Journal, 12: 951-961.ISSN 1818-4952.
Bagheri, M, Sulaiman, WNA & Vaghefi, N 2013, Application of geographic information system technique and analytical hierarchy process model for land-use suitability analysis on coastal area. Journal of Coast Conservation, 17: 1-10.
Blackard, JA & Dean, DJ 1999, Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables. Computers and Electronics in Agriculture, 24: 131-151.
Chapman, DF, Kenny, SN, Beca, D & Johnson, IR 2008, Pasture and forage crop systems for non-irrigated dairy farms in Southern Australia. 1. Physical production and economic performance. Agricultural Systems, 97: 108-125.
Collins, MG, Steiner, FR & Rushman, MJ 2001, Land suitability analysis in the United States: historical development and promising technological achievements. Environmental Management, 28: 611-621.
Crush, JR, Woodward, SL, Eerens, JP & Mac Donald, KA 2006, Growth and milksolids production in pastures of older and more recent ryegrass and white clover cultivars under dairy grazing. New Zealand Journal of Agricultural Research, 49: 119–135.
Dai, E, Fu, S, Shi, W, Cheung, C & Shaker, A 2005, Modeling change-pattern-value dynamics on land use: An integrated GIS and artificial neural networks approach. Environmental Management, 36: 576-591.
De Kroon, H, Fransen B, Van Rheenen, JWA, Van Dijk, A & Kreulen, R 1996, High level of interment water translocation in two rhizomatous Carex species, as quantified be deuterium labeling. Oecolgia, 106: 73-84.
Ebrahimi, A, Miloti,T & Hoffmann, M 2010, A herbivore specific grazing capacity model accounting for spatio-temporal environmental variation: A tool for a more sustainable nature conservation and rangeland management. Ecological Modelling, 221: 900-910.
Gallardo, A & Schlesinger, WH 1992, Carbon and nitrogen imitations of soil microbial biomass in desert ecosystems. Biogeochemistry, 18:1-17.
Gavili, E, Vahabi, MR, Arzani, H & Ghasriani, F 2011, Production suitability assessment in rangeland by geographic information system (Case study: Fereidoonshahr, Isfahan Province). Journal of Applied RS & GIS Techniques in Natural Resource Science, 2: 63-76.
Gholami, V, Azodi, M & Taghvaye Salimi, E 2008, Modeling of karst and alluvial springs discharge in the central Alborz highlands and on the Caspian southern coasts. Caspian Journal of Environmental Sciences, 6: 41-45.
Gholami, V & Mohseni Saravi, M 2010, Effects of impervious surfaces and urban development on runoff generation and flood hazard in the Hajighoshan watershed. Caspian Journal of Environmental Sciences, 8: 1-12.
Gholami, V, Darvari, Z & Mohseni Saravi, M 2015, Artificial neural network technique for rainfall temporal distribu-tion simulation (Case study: Kechik region). Caspian Journal of Environmental Sciences, 13: 53-60.
Gholami, V, Asghari,A & Taghvaye Salimi , E 2016, Flood hazard zoning using geographic information system (GIS) and HEC-RAS model (Case study: Rasht City). Caspian Journal of Environmental Sciences, 14: 263-272.
Gholami, V, Khaleghi, MR, Sebghati, M 2017, A method of groundwater quality assessment based on fuzzy network-CANFIS and geographic information system (GIS), Applied Water Science 7: 3633-3647.
Goharnejad, A, Zare, A, Tahmasebi, P, Asadi, E & Ebrahimi, A 2015, A Grazing Capacity Model with Fuzzy Inference System in Semi-steppe Rangelands. Environment and Natural Resources Journal, 13: 1-13.
Havstad, KM, Peters, DPC, Skaggs, R, Brown, J, Bestelmeyer, B, Fredrickson, ED, Herrick, J & Wright, J 2007, Ecological services to and from rangelands of the United States. Ecological Economic, 64: 261-268.
Hooper, DU & Johnson, L 1999, Nitrogen limitation in dry-land ecosystems: responses to geographical and temporal variation in precipitation. Biogeochemistry, 46: 247-293.
Hourou, HNL & Hoste, CH 1977, Rangeland production and annual rainfall relations in the Mediterranean Basin and in the African Sahelo-Sudanian Zone. Journal of Range Management, 30: 181-189.
Izaurralde, RC, Thomson, AM, Morgan, JA, Fay, PA, Polley, HW & Hatfield, JL 2011, Climate impacts on agriculture: Implications for forage and rangeland production. Publications from USDA-ARS / UNL Faculty. Paper 1351.
Janssen, MA, Walker, BH, Langridge, J & Abel, N 2000, An adaptive agent model for analyzing co-evolution ofmanagement and policies in a complex rangeland system. Ecological Modelling, 131: 249-268.
Jayasinghe, PKSC & Yoshida, M 2009, GIS-based neural network modeling to predict suitable area for beetroot in Sri Lanka: Towards sustainable agriculture. Journal of Developments in Sustainable Agriculture, 4: 165-172.
Keshavarzi, A, Sarmadian , F, Omran, EE& Iqbal, M 2015, A neural network model for estimating soil phosphorus using terrain analysis. The Egyptian Journal of Remote Sensing and Space Science, 18: 127–135.
Li, B, Shibuya, T, Yogo, Y, hara, T & Matsuos, K 2002, Effects of light quantity and quality on growth and reproduction of a clonal sedge, Cypers esculentus. Plant Species Biology, 16: 69-81.
Li, X & Yeh, AGO 2002, Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Science, 16: 323-343.
LotfiAnari, P, SharifiDarani, H & Nafarzadegan, AR 2011, Application of ANN and ANFIS models for estimating total infiltration rate in an arid rangeland ecosystem. Research Journal of Environmental Sciences, 5: 236-247.
Malczewski, J 2004, GIS-Based land-use suitability analysis: A critical overview. Program Planning, 62: 3-65.
Mas, JF, Puig, H, Palacio, JL & Sosa-López, A 2004, Modelling deforestation using GIS and artificial neural networks. Environmental Modelling & Software,19: 461–471.
Mirjalali, A 2011, Effects of enclosure, rest-delayed and continuous grazing treatments on production rate and vegetation cover of Sadr Abad Nodoushan, Yazd Pastures, Iran. Journal of Rangeland Science, 1: 167-174.
Moghadam, MR 2007, Range and range management. University of Tehran Press, Iran, 237 p.
Mousel, EM, Schacht, WH, Reece PE, Herron, AE & Koehler, A 2011, Vegetation production responses to October grazing in the Nebraska Sand hills. Rangeland Ecology and Management, 64: 208-214.
Nyachieo, J 2016, Promising practices in supporting management of water resources in pastoral areas, Agriculture and Food Security Network, 1-7.
Obade, VP & Lal, R 2013, Assessing land cover and soil quality by remote sensing and geographical information systems (GIS). Catena, 104: 77–92.
Perez, C, Roncoli, C, Neely, C & Steiner, JL 2007, Can carbon sequestration markets benefit low-income producers in semi-arid Africa? Potentials and challenges. Agricultural Systems, 94: 2–12.
Picardi, AC 1975, A system analysis of pastoralism in the West African Sahel. PhD and MSc. Dissertation, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA, 337 p.
Pijanowski, BC, Brown, D, Shellito, B & Manik, G 2002, Using neural networks and GIS to forecast land use changes: A land transformation model. Computers, Environment and Urban Systems, 26: 553-575.
Pijanowski, BC, Pithadia, S, Shellito, BA, Alexanderidis, K & Seifert, WW 2005, Calibrating a neural network-based urban change model for two metropolitan areas of the Upper Midwest of the United States. International Journal of Geographical Information Science, 19: 197-215.
Ramankutty, N, Evan, AT, Monfreda, C & Foley, JA 2008, Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Global Biogeochemical Cycles, 22: 1-19.
Rutunga, V, Janssen, BH, Mantel, S & Janssens, M 2007, Soil use and management strategy for raising food and cash output in Rwanda. Journal of Food Agriculture & Environment, 5: 434-441.
Sahour, H, Mokhtari, A & Tehrani, EN 2014, Effects of land use/land cover changes on surface runoff (A case study in Siahroud watershed, Iran). Elixir Remote Sensing. 74: 26867-26870.
Sahour, H, Sultan, M, Vazifedan, M, Abdelmohsen, K, Karki, S, Yellich, JA, Gebremichael, E, Alshehri, F & Elbayoumi, T 2020, Statistical applications to downscale GRACE-derived terrestrial water storage data and to fill temporal gaps. Remote Sensing, 12(3), p.533.
Skidmore, AK, Turner, BJ, Brinkhof, W & Knowles, E 1997, Performance of a neural network: mapping forests using GIS and remotely sensed data. International Journal of Photogrammetric Engineering and Remote Sensing, 63: 501-514.
Stafford Smith, M 1996, Management of rangelands: Paradigms at their limits, in the ecology and management of grazing systems, edited by J Hodgson & A Illius. Wallingford, Oxford shire, J, Hodgeson, pp. 325-356.
Stoddart, LA, Smith, AD & Box, TW 1975, Range management. McGraw-Hill, New York, 532 p.
Stuefer, JF & Huber, H 1998, Differential effects of light quantity and spectral light quality on growth, morphology and development of two stoloniferous Potentilla species. Oecolgia, 117: 1-8.
Vetter, S 2004, Rangelands at equilibrium and non-equilibrium recent developments in the debate around rangeland ecology and management. Published by the Program for Land Agrarian Studies (PLAAS), 80 p. | ||
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