List of Articles Spatial variability

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  1 - Comparing the applicability of some geostatistical methods to predict variability of some soil physical properties
  Jalal Mahmoudi fatemeh Zareian Mohamad Reza Javadi Nazila khorsandi
  Reasonable estimation of soil physical properties is very important for optimal management of soil and water resources. Estimation of soil physical properties is usually time consuming and expensive. Geostatistical methods can be used as suitable tools to esti More

  Reasonable estimation of soil physical properties is very important for optimal management of soil and water resources. Estimation of soil physical properties is usually time consuming and expensive. Geostatistical methods can be used as suitable tools to estimate such properties. In order to analyze spatial variability of soil properties in Dareh Viseh rangelands, a number of 78 soil samples from 0-30cm soil depth were taken and transferred to laboratory. Some soil properties including clay, silt, sand and bulk density were measured in laboratory. After normalizing the data, the semivariograms were obtained and evaluated. The Kriging, inverse distant weighting and radial basis function methods were then evaluated for the obtained data. To compare these methods, the cross validation method was used by statistical parameters of Mean Absolute Error (MAE) and Mean Bias Error (MBE). The results showed that the Kriging method can provide more reseanable predictions for silt, sand and bulk density, while the radial basis function provides better estimate to predict clay content in the study area. Manuscript profile
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  2 - Estimating the Spatial Distribution of Above-ground Carbon of Zagros Forests using Regression Kriging, Geographically Weighted Regression Kriging and Landsat 8 imagery
  somayeh izadi Hormoz Sohrabi
  10.22034/jest.2020.48928.4887
  Background and Objective: Estimating aboveground carbon (AGC) of forest is a fundamental task for sustainable management of forest ecosystems; therefore, there is a critical need for appropriate approaches for quantifying of AGC. The most commonly used approaches for es More

  Background and Objective: Estimating aboveground carbon (AGC) of forest is a fundamental task for sustainable management of forest ecosystems; therefore, there is a critical need for appropriate approaches for quantifying of AGC. The most commonly used approaches for estimating include global regression models that estimate the target variable over a wide range using cost-effective auxiliary data. Traditional regression models with fixed regression coefficients at all locations do not consider heterogeneity and spatial structure in modeling. The objective of this study is estimating the AGC using Regression Kriging, Geographically Weighted Regression Kriging and Landsat 8 data and compare methods. Material and Methodology: The study was carried out in part of Zagros Forest, in Kohgiluyeh and Boyer-Ahmad Province. Totally, 184 plots (30×30 meters) surveyed and AGC were calculated by allometric equations. 32 variables were extracted from Landsat 8 as auxiliary data in the modeling process. The assessment of accuracies of methods was evaluated by K-fold cross validation via criteria such as coefficient of variation (R2), root mean square error (RMSE). Findings: The results showed that Geographically Weighted Regression Kriging (R 2 = 0.66, RMSE= 21) had a better performance compared to Regression Kriging. Discussion and Conclusion: Hybrid methods with heterogeneity and spatial correlation can be a good alternative to early regression methods for estimating aboveground carbon (AGC). Manuscript profile
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  3 - Mapping Features of Surface and Depth, Soil Profiles by Using Geostatistical Techniques in Part of Qazvin Plain
  abbas taati Fereydoon Sarmadian hamidreza Motaghian Seyed rohollah mousavi
  Understanding the spatial variability of soil surface and depth features and capabilities in order to obtain detailed information about land and using them to the best and the most profitable type of productivity is essential in sustainable management of soil resources. More

  Understanding the spatial variability of soil surface and depth features and capabilities in order to obtain detailed information about land and using them to the best and the most profitable type of productivity is essential in sustainable management of soil resources. This study aimed on geostatistical mapping of some surface and depth features of soil profiles in 17,000 hectares of the plain of Qazvin. For this purpose, regular grid sampling pattern, with intervals 1300 × 1300 was selected. After drilling 61 profiles, the soil sampling were taken from soil horizons. Features of ECs, pH, clay, silt and sand percentage of    0-30 and 0-100 cm was calculated as an average weight in the depths. In the depths of the soil profiles, electrical conductivity and pH were the highest and the lowest coefficient of variation. Spatial variability characteristics by using vario-gram and the nugget Effect variance to sill was studied and for mapping features of Kriging was used in ArcGIS 10 software. The results showed in the both depths that exponential was the best model for silt and pH characteristics and for the EC, clay and silt Gaussian model was the best. Class properties have been studied for moderate to strong spatial dependence is the spatial dependence for all features of more than 1430 meters respectively. Maps of kriging demonstrated that the distribution of large amounts of soil in the area is that because of the extensive changes to the sheer size of the region can, in different soil management conditions, among them. Manuscript profile
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  4 - Multivariate geostatistical analysis in assessment of aerosols (Case study: Bushehr)
  Tayebeh Tabatabaei Abdolreza Karbassi Faramarz Moatar Seyed Masoud Monavari
  The mean aerosols samples in three periods of ten stations were taken from Bushehr region, to characterize the spatial variability and concentration of As, Cd, Co, Fe, Ni, Pb and V. The geostatistics and geographic information system (GIS) techniques were applied, and t More

  The mean aerosols samples in three periods of ten stations were taken from Bushehr region, to characterize the spatial variability and concentration of As, Cd, Co, Fe, Ni, Pb and V. The geostatistics and geographic information system (GIS) techniques were applied, and the disjunctive kriging was used to map the spatial patterns of the seven heavy metals. Meanwhile, Principal component analysis (PCA) and correlation matrix (CA) were used for the data processing. The results of Nug/Sill ratios for the seven metals showed that spatial dependent is moderate (0.25-0.75), that indicative the effects of natural factors such as parent material and topography. Meanwhile, the disjunctive kriging technique was used to quantify their concentration distribution. Combined with the results of PCA, 7 heavy metals could be divided into 3 factors. D1 was the metals, i.e., As, Co, Ni, Pb, V. Cd was in D2, Fe in D3. This results show the concentrations of 7 heavy metals were mainly controlled by the external factors. These results will contribute to the management of regional environment. Manuscript profile