leila khalasiahwazi; Mohammad Ali Zare Chahouki
Abstract
Zygophyllum atriplicoides is one of the most important rangeland plants that often seen as associated species and rarely seen as the dominant species in ranglands rangelands which is very critical for soil in starting and ending points of each transect. Measured soil properties (included gravel, ...
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Zygophyllum atriplicoides is one of the most important rangeland plants that often seen as associated species and rarely seen as the dominant species in ranglands rangelands which is very critical for soil in starting and ending points of each transect. Measured soil properties (included gravel, texture, organic matter, lime, pH and electrical conductivity) and physiography (elevation and slope) is measured also. By importing the information layers in appropriate model and using necessary statistical analysis in ENFA model, the map of its potential habitat has been created. The results showed that 25200 hectares of study site is potential habitat of Zygophyllum atriplicoides which is 34 percent of study site. To evaluate the verity of this model, Boyce index has been used and model rectitude in this test was determined as 87.2 percent. The result of this model is shown that PH and Lime are the main effective factor to determine potential suitable of this plant species.
Mohammad Ali Zare Chahouki; Lyla Khalsi Ahvazi; Hossein Azarnivand
Abstract
The aim of this study was providing plant species predictive habitat models by using logisticregression method. For this purpose, study area conducted in north east rangelands of Semnanmodeling vegetation data in addition to site condition in formation including topography, and soil wasprepared. sampling ...
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The aim of this study was providing plant species predictive habitat models by using logisticregression method. For this purpose, study area conducted in north east rangelands of Semnanmodeling vegetation data in addition to site condition in formation including topography, and soil wasprepared. sampling was done within each unit of sampling parallel transects and 1 vertical transectwith 750m length, each containing 15 quadrates (according to vegetation variations) were established.Quadrate size was determined for each vegetation type using the minimal area method. Soil sampleswere taken from 0-20 cm and 20-80 cm in starting and ending points of each transect. Logesticregression (LR) techniques were implemented for plant species predictive modeling. To plantpredictive mapping, it is necessary to prepare the maps of all affective factors of models. To mappingsoil characteristics, geostatistical method was used based on obtained predictive models for eachspecies (through LR method). The accuracy of the predicted maps was tested with actual vegetationmaps. In this study, the adequacy of vegetation type mapping was evaluated using kappa statistics.Predictive maps of Astragalus spp. ( κ =0.86), Halocnemum strobilaceum ( κ =0.51), Zygophylumeurypterum ( κ =0.58) and Seidlitzia rosmarrinus ( κ =0.6) with narrow amplitude is as the same ofactual vegetation map prepared for the study area. Predictive model of Artemisia sieberi ( κ =0.33),due to its ability to grow in most parts of north east rangeland of Semnan with relatively differenthabitat condition, is not possible.
Mohammad Ali Zare Chahouki; Lyla Khalsi Ahvazi; Hossein Azarnivand; Asghar Zare Chahouki
Abstract
The aim of this study preparation of the predicted soil maps by using kriging and Inverse Distance Weighting methods in east rangeland of Semnan. Sampling was done within each unit of sampling parallel transects and 1 vertical transect with 750m length. Soil samples were taken from 0-20 cm in starting ...
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The aim of this study preparation of the predicted soil maps by using kriging and Inverse Distance Weighting methods in east rangeland of Semnan. Sampling was done within each unit of sampling parallel transects and 1 vertical transect with 750m length. Soil samples were taken from 0-20 cm in starting and ending points of each transect. There were used kriging and Inverse Distance Weighting methods by Gs+ and GIS software to predict clay, sand, lime, EC and available moisture factors. For comparing these methods, cross validation were used by statistical parameters of MAE and MBE. Results showed that kriging method is better than Inverse Distance Weighting method in all factors except clay factor.