Alireza Arzani; Zarrintaj Alipour; Farhad Taghipour; Ahmad Akhiani
Volume 25, Issue 2 , August 2018, , Pages 324-334
Abstract
Land salinization is a major and destructive problem in the agricultural sector, which must be controlled by proper scientific management. The first step in this way is to identify and map the saline areas. Over the past few decades, the use of geostatistics and remote sensing techniques ...
Read More
Land salinization is a major and destructive problem in the agricultural sector, which must be controlled by proper scientific management. The first step in this way is to identify and map the saline areas. Over the past few decades, the use of geostatistics and remote sensing techniques has been developed to map the soil salinity and monitor its changes. The purpose of this study was to compare the capability of different geostatistics methods to prepare soil surface salinity maps in a part of the Meyami plain, Semnan province. To do this, 225 soil samples were taken from a depth of 0-30 cm of soil from the intersection of regular grid lines of 600 * 600 meters using GPS. EC, pH and clay of soil samples were measured. The results showed that the Kriging method with an average absolute error of 2.29 was more accurate than other interpolation methods and, vice versa, the Spline Thin plate method with an average absolute error of 4.38 had the lowest accuracy. According to the map prepared, the highest, lowest and mean salinity in the Meyami plain were recorded to be 15.69 ds/m, 2.12 ds/m, and, 5.24 ds/m, respectively.
Narges Naseri Hesar; Mohammad ali Zare chahouki; Mohammad Jafari
Volume 23, Issue 2 , September 2016, , Pages 310-299
Abstract
Spatial correlation is the first step in the interpolation of field data and mapping of soil properties.The aim of this research was to study the efficiency of two spatial statistics methods i.e., Kriging and inverse distance weighting for mapping of soil properties. Five sampling units were selected ...
Read More
Spatial correlation is the first step in the interpolation of field data and mapping of soil properties.The aim of this research was to study the efficiency of two spatial statistics methods i.e., Kriging and inverse distance weighting for mapping of soil properties. Five sampling units were selected in the region, and the location of soil profiles was so determined to cover the whole area. In each unit, six profiles and totally 30 soil profiles were dug in the whole area. Soil samples were taken from two depths of 0-20 cm and 20-80cm. Soil variables including gravel, clay, silt, lime, organic matter, pH and EC were measured in both soil depths. In the GS+ software, the accuracy of two spatial statistics methods was tested using cross validation with the help of two statistical parameters: MAE and MBE. According to the results, MAE and MBE, related to the Kriging method, for the majority of soil parameters, are less than that of inverse distance weighting method; therefore, Kriging is a more accurate method to interpolate soil properties.
Jalal Abdollahi; Mohammad hasan Rahimian
Volume 14, Issue 2 , January 2007, , Pages 156-170
Abstract
Remotely sensed data are able to monitor some characteristics of the environment and also their spatial structure. The latter one is the first and main step in field data interpolations. Therefore, spatial analysis of some field data is possible by employing of related satellite data bands. In this study, ...
Read More
Remotely sensed data are able to monitor some characteristics of the environment and also their spatial structure. The latter one is the first and main step in field data interpolations. Therefore, spatial analysis of some field data is possible by employing of related satellite data bands. In this study, as an example, Landsat ETM+ thermal band (band 6) was acquired to determine the spatial structure of surface temperature distribution. For the purpose of evaluation and selection of the best interpolation model, the band 6 data was crossed with a regular sampling grid and therefore, a dataset was constructed. Using geo-statistical analysis, empirical semi-variogram was calculated and various mathematical models were fitted to its points (e.g. gaussian, exponential, circular and spherical). Afterwards, the fitted models were applied to generate different temperature distribution maps using kriging interpolation approach. Finally, the optimum model which could better predict temperature changes and distribution was recognized. Result of the study shows that the exponential model would be better to predict and estimate surface temperature in un-sampled locations in the area of studied. So, the model can be used for interpolation of field temperature data with a high confidence level. The represented method can be developed for all the other environmental factors which could better characterized by remotely sensed data, like minimum and maximum temperature, evapotranspiration and so on.