Estimation of the rangeland cover by coupling artificial neural network (ANN) and geographic information system (GIS) in Baladeh Ranglands

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ahmadi jolandan maryam; Dianati Tilaki Ghasemali; GHOLAMI VAHID

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Journal: JOURNAL OF PLANT ECOSYSTEM CONSERVATION Year:2018 | Volume:6 | Issue:12 Page(s): 153-175

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Title

Estimation of the rangeland cover by coupling artificial neural network (ANN) and geographic information system (GIS) in Baladeh Ranglands

Author(s)

ahmadi jolandan maryam | Dianati Tilaki Ghasemali | GHOLAMI VAHID | Issue Writer Certificate

Keywords

Rangeland vegetationQ3

ModelingQ2

MLPQ2

VerificationQ3

Mazandaran ProvinceQ2

Abstract

Rangeland is one of the important natural resources in different aspects such as, forage production, livestock, promenade and soil and water conservation. Therefore, it is necessary to study rangelands for their management and conservation. Here, Field studies of rangelands are time consuming and costly. So, it was common models application to estimate rangelands vegetation parameters. In this study, ANN was used to estimate rangelands cover percent and GIS was used as a pre-processing and post-processing in Modeling respectively in the Baladeh rangelands (Mazandaran Province). Multi-layer percepetron (MLP) network and multivariate regression method were used to estimate rangelands cover percent (training stage). In Modeling process, sampling and estimation cover percent was performed in the 127 sites. Also, the affecting factors in cover percent were evaluated such as; topography, climatic factors, soil and mankind factors. Multivariate regression and stepwise method were used to simulate rangeland cover in SPSS software. An optimal network was presented by using cover percent as desired parameter and the affecting factors in cover percent as the network inputs. Then, optimum network was verified (test stage). The study area was divided with the pixels 1×1 km (raster format) in GIS medium. Then, the model input layers were combined and a raster layer was generated that included the model inputs values and geographic coordinate. The values of pixels (model inputs) were entered into ANN with geographic coordinate. The results showed that ANN has a higher efficiency and accuracy (model test; Rsqr=0. 72) than multivariate regression method (model test; Rsqr=0. 6) in rangeland cover Modeling. In the next step, cover percent was simulated using the verified optimum network for all of the study rangelands. Finally, the results of ANN simulation were entered into GIS and cover percent map was generated based on the simulated results of ANN. The results showed that coupling of ANN and GIS has an high capability (test stage: Rsr= 0. 72) in rangelands cover percent Modeling.

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APA: Copy

ahmadi jolandan, maryam, Dianati Tilaki, Ghasemali, & GHOLAMI, VAHID. (2018). Estimation of the rangeland cover by coupling artificial neural network (ANN) and geographic information system (GIS) in Baladeh Ranglands. JOURNAL OF PLANT ECOSYSTEM CONSERVATION, 6(12 ), 153-175. SID. https://sid.ir/paper/259568/en

Vancouver: Copy

ahmadi jolandan maryam, Dianati Tilaki Ghasemali, GHOLAMI VAHID. Estimation of the rangeland cover by coupling artificial neural network (ANN) and geographic information system (GIS) in Baladeh Ranglands. JOURNAL OF PLANT ECOSYSTEM CONSERVATION[Internet]. 2018;6(12 ):153-175. Available from: https://sid.ir/paper/259568/en

IEEE: Copy

maryam ahmadi jolandan, Ghasemali Dianati Tilaki, and VAHID GHOLAMI, “Estimation of the rangeland cover by coupling artificial neural network (ANN) and geographic information system (GIS) in Baladeh Ranglands,” JOURNAL OF PLANT ECOSYSTEM CONSERVATION, vol. 6, no. 12 , pp. 153–175, 2018, [Online]. Available: https://sid.ir/paper/259568/en

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