Volume 1, Issue 3 (Autumn 2020 2020)                   JGSMA 2020, 1(3): 1-17 | Back to browse issues page


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Mahmoudvand S, Khodayari H, Tarnian F. Mapping Bioclimatic Variables Using Geostatistical and Regression Techniques in Lorestan Province. JGSMA. 2020; 1 (3) :1-17
URL: http://gsma.lu.ac.ir/article-1-74-en.html
1- M.Sc. Graduated of Biology Department, Faculty of Basic Sciences, Lorestan University, Khorramabad, Iran.
2- Assistant Professor of Biology Department, Faculty of Basic Sciences, Lorestan University, Khorramabad, Iran. , khodayari.h@lu.ac.ir
3- Assistant Professor of Natural Resources Engineering and Watershed Management Department, Faculty of Agriculture, Lorestan University, Khorramabad, Iran.
Abstract:   (1259 Views)
Bioclimatic variables are one of the most important environmental variables that used in mapping and species distribution modeling for the management and conservation of vegetation and species cultivation. In order to provide bioclimatic maps, long-term climate data of 49 weather stations were used during the years 1952 to 2017 to extract 19 bioclimatic variables. Geostatistics methods (Kriging and Cokriging) and Multiple Linear Regression model were used to create 19 bioclimatic variables in Lorestan Province. Correlation ratio was used to select the best interpolation model. Also, Cross-validation was used to validate the interpolation method. Root Mean Square Error (RMSE) and the Root Mean Square Standardized Error (RMSSE) were used to select the best interpolation method. Based on the results, the best interpolation method for maping Bio4, Bio5, Bio7, Bio12, Bio13, Bio15, Bio16, Bio17 was Kriging method due to lower error values of RMSE and RMSSE for and Bio18 and the best interpolation method for maping of Bio1, Bio2, Bio3, Bio6, Bio8, Bio9, Bio10, and Bio11 was Cokriging method. Multiple Linear Regression model was also the best interpolation method for Bio19. Based on the results of this study, the use of an elevation auxiliary variable and climatic factor can increase the accuracy of the evaluation of interpolation methods to create accurate maps for modeling of species distribution.
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Type of Study: Research | Subject: Special
Received: 2020/10/2 | Accepted: 2020/12/5

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