APPLICATION OF SPATIAL STATISTICAL METHODS IN PREDICTIVE MODELS OF PLANT SPECIES HABITAT

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ZARE CHAHOUKI M.A.; ZARE ERNANI M.; ZARE CHAHOUKI A.; KHALASI AHVAZI L.

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Journal: Journal of Arid Biome Year:2010 | Volume:1 | Issue:1 Page(s): 13-24

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Title

APPLICATION OF SPATIAL STATISTICAL METHODS IN PREDICTIVE MODELS OF PLANT SPECIES HABITAT

Author(s)

ZARE CHAHOUKI M.A. | ZARE ERNANI M. | ZARE CHAHOUKI A. | KHALASI AHVAZI L. | Issue Writer Certificate

Keywords

CROSS VALIDATIONQ3

GEOSTATISTICSQ2

PREDICTIVE MODELINGQ3

POSHTOKOUH RANGELANDSQ3

YAZDQ3

Abstract

The main objective of this study was application of spatial statistics as a tool for model based prediction of vegetation types.Sampling method was randomized-systematic. Quadrate size was determined for each vegetation type using the minimal area; hence suitable quadrate size for different species was determined 1*2m–10-10m (2-100 m2). Within each unit 3-5 parallel transects with 300-500 length, each containing 30-50 quadrates (according to vegetation variations) were established. Soil samples were taken from 0-30 and 30-80 cm in starting and ending points of each transect. Measured soil properties included gravel, texture, available moisture, saturation moisture, organic matter, lime, gypsum, pH, electrical conductivity and soluble ions (Na+, K+, Mg2+, Ca2+, Cl-, CO2-3, HCO-3 and So2-4). Logistic Regression (LR) techniques were implemented for PREDICTIVE MODELING of Cornulaca monachantha. To mapping soil characteristics, spatial statistical methods of point-Kriging, Normal Distance Weighting and Inverse Distance Weighting were used to predict soil factors by using GS+ and ArcGIS softwares. Finally, CROSS VALIDATION technique were used for comparing the above mentioned methods by considering the statistical parameters of MAE and MBE. It can be concluded from the results that the point Kriging method is the best method among the others in all of the factors. Results shows that the point Kriging method by MAE 1.56 and MBE -0.048 in gypsum, and gravel factor by MAE 0.176 and MBE 0.006 (0-30 cm depth) is better than the others and sampling method is effective in accuracy of geostatistical method. Predictive map of C. monachanthawhich has narrow amplitude, with 98% Kappa coefficient, has high accordance with the actual vegetation map prepared for the study area.

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APA: Copy

ZARE CHAHOUKI, M.A., ZARE ERNANI, M., ZARE CHAHOUKI, A., & KHALASI AHVAZI, L.. (2010). APPLICATION OF SPATIAL STATISTICAL METHODS IN PREDICTIVE MODELS OF PLANT SPECIES HABITAT. ARID BIOM SCIENTIFIC AND RESEARCH JOURNAL, 1(1), 13-24. SID. https://sid.ir/paper/199772/en

Vancouver: Copy

ZARE CHAHOUKI M.A., ZARE ERNANI M., ZARE CHAHOUKI A., KHALASI AHVAZI L.. APPLICATION OF SPATIAL STATISTICAL METHODS IN PREDICTIVE MODELS OF PLANT SPECIES HABITAT. ARID BIOM SCIENTIFIC AND RESEARCH JOURNAL[Internet]. 2010;1(1):13-24. Available from: https://sid.ir/paper/199772/en

IEEE: Copy

M.A. ZARE CHAHOUKI, M. ZARE ERNANI, A. ZARE CHAHOUKI, and L. KHALASI AHVAZI, “APPLICATION OF SPATIAL STATISTICAL METHODS IN PREDICTIVE MODELS OF PLANT SPECIES HABITAT,” ARID BIOM SCIENTIFIC AND RESEARCH JOURNAL, vol. 1, no. 1, pp. 13–24, 2010, [Online]. Available: https://sid.ir/paper/199772/en

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