The main objective of the study is application of spatial statistics as a tool for model-based prediction of vegetation types. Method of selecting Samples was systematic-randomized. Quadrate size was determined for each vegetation type using the minimal area method; hence suitable quadrate size for different species ranged from 12m to 1010m (i. e., 2-100 m2). Within each unit, 3-5 parallel transects with 300 to 500 m long, each containing 30-50 quadrates (according to the vegetation variations) were established. Soil samples were taken at soil depth of 0-30 and 30-80 cm at the starting and ending points of each transect. Measured soil properties include gravel, texture, available soil moisture, saturation moisture, organic matter, lime, gypsum, pH, electrical conductivity and soluble ions (such as Na+, K+, Mg2+, Ca2+, Cl-, , and ). Logistic Regression (LR) technique was applied for predictive modeling of Cornulaca monachantha. To map soil characteristics, spatial statistical methods of point-Kriging, Normal Distance Weighting and Inverse Distance Weighting were used to predict soil factors using GS+ and ArcGIS softwares. Finally, cross validation technique were used to compare the above mentioned methods by considering the statistical parameters of MAE and MBE. It can be concluded that the point Kriging method is the best method among the others in all of the factors. Results show that the point Kriging method by MAE of 1.56 and MBE of -0.048 in gypsum, and gravel factor by MAE of 0.176 and MBE of 0.006 (0-30 cm depth) is better than the others and the sampling method is effective in accuracy of geostatistical method. Predictive map of C. monachantha which has narrow amplitude, with Kappa coefficient of 0.98, has high accuracy in accordance with the actual vegetation map prepared for the study area.
Zare Chahouki, M., Zare Ernani, M., Zare Chahouki, A., & Khalasi Ahvazi, L. (2010). Application of spatial statistical methods in predictive models of plant species habitat. Journal of Arid Biome, 1(1), 13-24.
MLA
M.A. Zare Chahouki; M. Zare Ernani; A. Zare Chahouki; L. Khalasi Ahvazi. "Application of spatial statistical methods in predictive models of plant species habitat". Journal of Arid Biome, 1, 1, 2010, 13-24.
HARVARD
Zare Chahouki, M., Zare Ernani, M., Zare Chahouki, A., Khalasi Ahvazi, L. (2010). 'Application of spatial statistical methods in predictive models of plant species habitat', Journal of Arid Biome, 1(1), pp. 13-24.
VANCOUVER
Zare Chahouki, M., Zare Ernani, M., Zare Chahouki, A., Khalasi Ahvazi, L. Application of spatial statistical methods in predictive models of plant species habitat. Journal of Arid Biome, 2010; 1(1): 13-24.
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