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Showing 12 results for Kriging
Volume 1, Issue 3 (9-2023)
Abstract
Following years of contamination, rivers may experience significant levels of heavy metal pollution. Our research aims to pinpoint hazardous areas in these rivers. In our specific case, we focus on the floodplains of the Meuse River contaminated with zinc (Zn). Elevated zinc concentrations can lead to various health issues, including anemia, rashes, vomiting, and stomach cramping. However, due to limited sample data on zinc concentrations in the Meuse River, it becomes imperative to generate missing data in unidentified regions. This study employs universal Kriging in spatial data mining to investigate and predict unknown zinc pollutants. The semivariogram serves as a valuable tool for illustrating the variability pattern of zinc. To predict concentrations in unknown regions, the model captured is interpolated using the Kriging method. Employing regression with geographic weighting allows us to observe how stimulus-response relationships change spatially. Various semivariogram models, such as Matern, exponential, and linear, are utilized in our work. Additionally, we introduce Universal Kriging and geographically weighted regression. Experimental findings indicate that: (i) the Matern model, determined by calculating the minimum error sum of squares, is the most suitable theoretical semivariogram model; and (ii) the accuracy of predictions is visually demonstrated by projecting results onto a real map.
Volume 2, Issue 3 (9-2014)
Abstract
Since the change of land use accrued in the Iran, especially in northern Iran, this research aims tocompare the spatial variability of soil properties in three adjacent land uses including cultivated by wheat lands, grazing lands and forest Lands covered by juniperus sp, fagus orientalis, quercus castanifolia, and acer velotinum species in kiasar region, Mazandaran Province, northern Iran. Some of soil features, i.e. pH, CaCO3, total nitrogen (TN), soil organic carbon (SOC), electric conductivity (EC), percentage of silt, clay and sand contents and saturation moisture content(SM) were measured at a grid with 20 m sampling distance on the top soil (0 – 30 cm depth). Accordingly, total of 147samples were taken from 49 soil sites. The normality of data was examined by the tests of normality. Then, data were analyzed by using of geostatistics approach. The results showed that spatial distribution of many soil properties could be well described by spherical model in the forest and exponential model in the cultivated and grazing lands. Spatial dependences were the highest for SOC, EC and the lowest for silt, (SOC and silt) in the forest method and grazing lands, respectively. Deforestation and conversion to cultivated and grazing lands decreased spatial dependence of soil properties.
Volume 4, Issue 1 (1-2002)
Abstract
Spatial patterns of soil fertility parameters, and other extrinsic factors need to be iden-tified to develop farming practices that match agricultural inputs with local crop needs. Little is known about the spatial structure of yield and weed density across fields. In this study, geostatistics was used to describe and map spatial patterns of soil total nitrogen, available phosphorus, available potassium, grain yield and density of Sisymbrium irio L. (tumble mustard), as a common annual weed of wheat fields at Shahre Kord university. The spatial continuity of each variable was examined by variogram function. The variograms showed that the distribution of all variables is not random but spatially-dependent as their estimated variogram values increase with increasing distance. The av-erage range values were 26.5, 23.4, 31.4, 27.7, and 27.2 m for total nitrogen, available phosphorus, available potassium, grain yield and weed density, respectively. Thus, the range beyond which the property is not longer spatially dependent was almost the same for total nitrogen, grain yield and weed density. This implied close spatial interactions among these variables over the field. Applying the variogram models with the kriging al-gorithm, the values for each variable were estimated on a 55 grid. The disrribution of all variables is spatially dependent and continuous over a short distance. Furthermore, the maps illustratc a joint spatial dependence between grain yield and weed density. Spatial patterns of soil properties identified by these geostatistical techniques are of great impor-tance in the fertility management of spatially variable soils. By studying the spatial struc-ture of yield and mapping, it could be used in determining different factors controlling yield over the field. Moreover, a better knowledge of annual or perennial weed density distribution over fields might be helpful in better designing long-term field experiments in weed control programs.
Volume 4, Issue 5 (12-2015)
Abstract
The common pistachio psylla Agonoscena pistaciae is a key pest of pistachio in Iran. A study was conducted to determine the spatial distribution ofpsyllanymphs and eggs in a 10 ha pistachio orchard in the Rafsanjan region, southeast of Iran. Three rows, each containing 33 trees (totally 99 trees), were randomly selected in the orchard based on a stratified sampling scheme. In each of the selected trees, three positions in the crown (top, middle and bottom) were considered. One leaf from each position as sampling unit (totally 297 samples) was clipped and number of nymphs and eggs were counted. Ordinary kriged maps were achieved for nymphs and eggs of the three positions using a variogram function. Results indicated the highest and lowest density of the nymphs occurred on the top and bottom positions of the crown, respectively. Eggs of the common pistachio psylla were laid mostly on the bottom of the pistachio crown.
Volume 8, Issue 1 (1-2019)
Abstract
Understanding the spatial dynamics of insect distributions provides useful information about their ecological requirements and can also be used in site-specific pest management programs. Interactions between prey and predator are spatially and temporally dynamic and can be affected by several factors. In this study, geostatistics was used to characterize the spatial variability of spotted alfalfa aphid, Therioaphis maculata Buckton and coccinellid lady beetles in alfalfa fields. Global positioning and geographic information systems were used for spatial sampling and mapping the distribution pattern of these insects. This study was conducted in three alfalfa fields with areas of 7.3, 3.1 and 0.5 ha and two growing seasons, 2013 and 2014. The 0.5 ha field was divided into 10 × 10m grids and 3.1 and 7.3 ha fields were divided into 30 × 30m grids. Weekly sampling began when height of alfalfa plants reached about 15cm and was continued until the cuttings of alfalfa hay. For sampling, 40 and 10 stems were chosen randomly in 30 × 30m and 10 × 10m grids, respectively and shaken into a white pan three times. Aphids and coccinellids fallen in the pan were counted and recorded. Semivariance analysis indicated that distribution of T. maculata and coccinellids was aggregated in the fields. Comparison of the distribution maps of aphid and lady beetles indicated that there was an overlap between the maps, but they did not coincide completely. This study revealed that relationship between spotted alfalfa aphid and lady beetles was spatially dynamic. These results can be used in biological control and site-specific management programs of T. maculata.
Volume 11, Issue 1 (2-2023)
Abstract
Aims: The availability of precipitation data plays an important role in many meteorological, hydrological and applications.
Materials & Methods: In this study, to improve precipitation maps and increase the accuracy of precipitation maps, linear regression, multivariate, and Kriging subsets were used. The data from 14 meteorological stations and IMERG images in the period of 20 years (2001 to 2020), digital elevation model, Latitude and Longitude maps were used. At first, based on regression in Minitab software, the relationship between air and ground parameters was taken. Finally, with the interpolation methods and based on the error coefficients, the best equations for predicting precipitation were determined and the spatial distribution of precipitation was obtained.
Findings: According to the results, six out of 13 models were selected because of low RMSE and high R2, R, and NS. In regression models where only one climatic or edaphic parameter was used, forecast accuracy was reduced. But in the models that were used in the regression elevation, Longitude, Latitude and IMERG parameters in combination with interpolation methods, the extracted data matched the real data with a slight difference. In this study, instead of the average of the input parameters, the maps of each parameter were used, increasing the accuracy of the forecast model to R2=0.8.
Conclusion: results showed that combining satellite precipitation products with interpolation methods led to a more accurate estimate of precipitation in the points without recording data will be precipitated and the multivariate regression method will be more accurate than the linear gradient.
Ali Elafri, Ismahan Halassi, Abdelah Aoues, Hanin Ghomrassi,
Volume 11, Issue 2 (6-2025)
Volume 11, Issue 2 (6-2025)
Abstract
We aim in this study to increase our knowledge of the Odonata in the Aures, an unexplored region of northeastern Algeria, using single-species occupancy model (spOccupancy R package) coupled with spatial interpolation technique (kriging ArcGis) to assess the relationships between elevation and odonatan species distribution. From time windows of about 90 days (June to August 2021), a total of 22 odonatan species belonging to 2 suborders (Anisoptera and Zygoptera) have been recorded in 15 sampling wet biotopes; among them the endangered Calopteryx exul. Our modelling shows that 62% of the odonatological community has a uniform probability of being present in the studied area. The probability of detecting a species is similar during each survey for 90% of the odonatological community except for the endangered Calopteryx exul (p ˂ 0.05) and Crocothemis erythraea (p ˂ 0.05). We also found that Ischnura graellsii and I. saharensis are the most common species; they are predicted to occur in more than 60% of sites, followed by Anax imperator, Orthetrum chrysostigma, and Platycnemis subdilatata, where they occur in about 50% of the wet biotopes sampled. Finally, our modelling revealed no evidence for a significant altitudinal variation (500 to 1900 meters above sea level) impact on both occupancy and detectability of the majority of the odonatan species, except for Crocothemis erythraea and Sympetrum fonscolombii. The kriging interpolation indicates that they are concentrated within the altitude range of 400 m to 1000 m.
Volume 15, Issue 7 (9-2015)
Abstract
In order to assess the effect of turbulence models in prediction of flow structure with adverse pressure gradient, steady state Reynolds-averaged Navier-Stokes (RANS) equations in an annular axisymmetric diffuser are solved. After selection of the best turbulence model, an approach for the shape optimization of annular diffusers is presented. The goal in our optimization process is to maximize diffuser performance and, in this way, pressure recovery by optimizing the geometry. Our methodology is the optimization through wall contouring of a given two-dimensional diffuser length and area ratio. The developed algorithm uses the CFD software: Fluent for the hydrodynamic analysis and employs surrogate modeling and an expected improvement approach to optimization. The non-uniform rational basic splines (NURBS) are used to represent the shape of diffuser wall with two to ten design variables, respectively. In order to manage solution time, the Kriging surrogate model is employed to predict exact answers. The CFD software and the Kriging model have been combined for a fully automated operation using some special control commands on the Matlab platform. In order to seek a balance between local and global search, an adaptive sample criterion is employed. The optimal design exhibits a reasonable performance improvement compared with the reference design.
Volume 16, Issue 1 (3-2016)
Abstract
In the present paper, to determine the pressure-dependent yield surface of polypropylene/nanoclay nanocomposites, the extended Drucker-Prager yield criterion is used and its parameters are derived by a combined experimental/numerical/optimization approach. In this method, the difference between the experimental and numerical results obtained from three-point bending test is minimized. In order to alleviate the burdensome numerical simulation, a surrogate model based on Kriging method is used to estimate the cost function. The optimum of this function is obtained by maximizing expected improvement method. Afterward, the results are verified by tension and compression tests. The results show that this method can substitute the complicated experimental tests which are normally employed to identify the extended Drucker-Prager parameters. Also, this method can be used to determine the mechanical properties of thermoplastic material such as tensile and compressive yield stresses and elastic modulus using only a three-point bending test. In addition, it is found that the volumetric change of thermoplastic during plastic deformation is significant and the non-associative, compared with the associative, plastic flow assumption is more proper for this material for the extended Drucker-Prager criterion.
Volume 19, Issue 4 (7-2017)
Abstract
In Iran, applying geostatistics to regional analysis is said to be in its early stages. The fundamental principle of this technique emphasizes the interpolation of hydrological variables in physiographical, instead of geographical, spaces. This paper deals with the adaptation, application, and comparison of two regional analysis methods based on geostatistics. In this study, data from 38 gauging stations located in the north of Iran were used to investigate the performance of geostatistical methods in two physiographical spaces. Two multivariate analysis methods, namely, Canonical Correlation Analysis (CCA) and Principal Components Analysis (PCA), were used to identify physiographical spaces. Gaussian and exponential models were selected as the best theoretical variogram models in CCA and PCA spaces, respectively. Ordinary and simple kriging geostatistical estimators were also used for regional estimations in both physiographical spaces. Using the interpolation methods in CCA and PCA spaces, regional flood estimations were made for different return periods (10, 20, 50, and 100 years). Finally, performance of both models was studied using five statistical indices. The results showed that both methods had similar and satisfactory performance; however, regional estimations in CCA had higher accuracy and less uncertainty than those in PCA-space. Furthermore, the results indicated that the ordinary kriging method had better performance than the simple kriging method in both spaces and the best interpolation efficiency was observed in the CCA space.
Volume 24, Issue 1 (1-2022)
Abstract
Worldwide, Iran is the first producer of pistachio, which is one of the most economically important agricultural products for this country. Idiocerus stali Fieber (Hemiptera: Cicadellidae) is one of the most important pests of this plant. Adults and nymphs pest feed on leaf tissues and fruit clusters, and they cause damage by sucking the sap. This pest has one generation per year. In this research, population fluctuations of the pistachio leafhopper associated to the temperature and humidity changes and its spatial distribution, using both statistical and geostatistical methods were studied in 2018-2019. The spatial distribution of all life stages in both years was cumulative according to Iwao model, whereas considering Taylor's power law model it was cumulative in 2018 and random in 2019. Considering coefficients' values, both models of Taylor's power law (R2= 0.93) and Iwao model (R2= 0.92) are appropriate for estimating the type of spatial distribution for this pest, however, Taylor model showed a better data fitting. Concerning geostatistics models, Kriging interpolation method was more accurate than Inverse Distance Weighting (IDW) and it was used to produce pest distribution maps. The movement process of adults, nymphs, and the sites of laying areas per week was precisely determined. Hence, contamination foci can be identified and used to apply appropriate management methods at the right time at a low cost.
Volume 25, Issue 4 (12-2021)
Abstract
Introduction
Due to technical and financial limitations, it is not possible to simultaneously provide high spatial and temporal resolution by a sensor. There is always a trade-off between the spatial and temporal resolution of the sensors. For studies such as estimating evapotranspiration, land surface temperature with high temporal and spatial resolution is required; however, estimating actual evapotranspiration with high temporal and spatial resolution by a single sensor is not possible. Since high spatial and temporal resolution together increase the reliability of analyzing and extracting information from the image, so the best way to overcome this problem is to downscale images to high temporal and spatial resolutions. Downscaling is the process of converting images with low spatial resolution to images with high spatial resolution. So far, several methods have been proposed for downscaling. These methods differ for downscaling of the reflectance and thermal bands. Many studies that have been conducted so far on the actual evapotranspiration estimation, indicate the efficiency of SEBAL algorithm for this purpose. Therefore, in this study, in order to calculate the actual evapotranspiration, the SEBAL model was used and the products of different downscaling methods were given as input to this model. Assessing the accuracy of actual evapotranspiration calculated using remote sensing data indicates the efficiency of products obtained from different methods. According to the studies conducted in this field, so far no study has been done on the combination of downscaled bands obtained from different downscaling methods applied on thermal data and non-thermal data in order to calculate the actual evapotranspiration. In this study, STARFM, ESTARFM and Regression algorithms were used to downscale the reflectance bands and SADFAT, Regression and Cokriging algorithms were used to downscale the thermal bands. Then the accuracy of the results was evaluated.
Methodology
The study area is Amirkabir agro-industry located in the south of Khuzestan province, one of the seven companies for the development of sugarcane cultivation and ancillary industries (longitude 48.287100, and latitude 31.029696 degrees). The gross land area of this agro-industry is 15000 hectares and its net area is 12000 hectares which is divided into several 25-hectare plots. In this research, the images of MODIS located on Terra satellite and the images of OLI and TIRS sensors of Landsat 8 satellite were used. It is worth noting that the Landsat image for time 2 was used to evaluate the simulation results. The downscaling algorithms used in this research included STARFM, ESTARFM, and REGRESSION algorithms were applied on reflectance bands and SADFAT, Regression and Cokriging algorithms were used for thermal band downscaling. In order to conduct this research, first, various downscaling methods were applied on MODIS images to be downscaled to the images with Landsat spatial resolution. Then, using MODIS downscaled images, evapotranspiration values were calculated for different combinations of downscaled data using SEBAL method and the results were compared and evaluated with evapotranspiration obtained from Landsat images acquired at the same date as MODIS data.
Results and discussion
In order to evaluate the results, the downscaled bands were visually and quantitatively compared with the corresponding bands of the Landsat image acquired on the same date. In order to compare these data quantitatively, the root mean square error (RMSE) and the coefficient of determination (R2) were used. According to the RMSEs, it can be concluded that the STARFM, ESTARFM, Regression, SADFAT and Cokriging downscaling algorithms all perform well. Among the methods applied to the reflectance bands, STARFM with the RMSE of 0.0180 had the best performance, followed by ESTARFM with the RMSE of 0.0186 and Regression with the RMSE of 0.0479. Among the methods applied to thermal bands, the SADFAT algorithm with the RMSE of 0.0224 had the best performance, followed by Cokriging with the RMSE of 0.0234 and Regression with the RMSE of 0.0464. It should be noted that the difference in outputs is very small, and given that the study area of this study is a homogeneous area of agricultural land cover including a single sugarcane crop. This issue can be the main reason for the close performance of downscaling methods and the high accuracy of their outputs. Moreover, according to the results obtained for evapotranspiration, ESTARFM / Regression, ESTARFM / SADFAT, STARFM / Regression and STARFM / SADFAT had the best performance with the lowest difference and the Regression / Cokriging method had the weakest performance, respectively.
Conclusion
This study can be concluded as follows:
Due to technical and financial limitations, it is not possible to simultaneously provide high spatial and temporal resolution by a sensor. There is always a trade-off between the spatial and temporal resolution of the sensors. For studies such as estimating evapotranspiration, land surface temperature with high temporal and spatial resolution is required; however, estimating actual evapotranspiration with high temporal and spatial resolution by a single sensor is not possible. Since high spatial and temporal resolution together increase the reliability of analyzing and extracting information from the image, so the best way to overcome this problem is to downscale images to high temporal and spatial resolutions. Downscaling is the process of converting images with low spatial resolution to images with high spatial resolution. So far, several methods have been proposed for downscaling. These methods differ for downscaling of the reflectance and thermal bands. Many studies that have been conducted so far on the actual evapotranspiration estimation, indicate the efficiency of SEBAL algorithm for this purpose. Therefore, in this study, in order to calculate the actual evapotranspiration, the SEBAL model was used and the products of different downscaling methods were given as input to this model. Assessing the accuracy of actual evapotranspiration calculated using remote sensing data indicates the efficiency of products obtained from different methods. According to the studies conducted in this field, so far no study has been done on the combination of downscaled bands obtained from different downscaling methods applied on thermal data and non-thermal data in order to calculate the actual evapotranspiration. In this study, STARFM, ESTARFM and Regression algorithms were used to downscale the reflectance bands and SADFAT, Regression and Cokriging algorithms were used to downscale the thermal bands. Then the accuracy of the results was evaluated.
Methodology
The study area is Amirkabir agro-industry located in the south of Khuzestan province, one of the seven companies for the development of sugarcane cultivation and ancillary industries (longitude 48.287100, and latitude 31.029696 degrees). The gross land area of this agro-industry is 15000 hectares and its net area is 12000 hectares which is divided into several 25-hectare plots. In this research, the images of MODIS located on Terra satellite and the images of OLI and TIRS sensors of Landsat 8 satellite were used. It is worth noting that the Landsat image for time 2 was used to evaluate the simulation results. The downscaling algorithms used in this research included STARFM, ESTARFM, and REGRESSION algorithms were applied on reflectance bands and SADFAT, Regression and Cokriging algorithms were used for thermal band downscaling. In order to conduct this research, first, various downscaling methods were applied on MODIS images to be downscaled to the images with Landsat spatial resolution. Then, using MODIS downscaled images, evapotranspiration values were calculated for different combinations of downscaled data using SEBAL method and the results were compared and evaluated with evapotranspiration obtained from Landsat images acquired at the same date as MODIS data.
Results and discussion
In order to evaluate the results, the downscaled bands were visually and quantitatively compared with the corresponding bands of the Landsat image acquired on the same date. In order to compare these data quantitatively, the root mean square error (RMSE) and the coefficient of determination (R2) were used. According to the RMSEs, it can be concluded that the STARFM, ESTARFM, Regression, SADFAT and Cokriging downscaling algorithms all perform well. Among the methods applied to the reflectance bands, STARFM with the RMSE of 0.0180 had the best performance, followed by ESTARFM with the RMSE of 0.0186 and Regression with the RMSE of 0.0479. Among the methods applied to thermal bands, the SADFAT algorithm with the RMSE of 0.0224 had the best performance, followed by Cokriging with the RMSE of 0.0234 and Regression with the RMSE of 0.0464. It should be noted that the difference in outputs is very small, and given that the study area of this study is a homogeneous area of agricultural land cover including a single sugarcane crop. This issue can be the main reason for the close performance of downscaling methods and the high accuracy of their outputs. Moreover, according to the results obtained for evapotranspiration, ESTARFM / Regression, ESTARFM / SADFAT, STARFM / Regression and STARFM / SADFAT had the best performance with the lowest difference and the Regression / Cokriging method had the weakest performance, respectively.
Conclusion
This study can be concluded as follows:
- All downscaling algorithms used in this research had an acceptable performance in simulating Landsat bands.
- Among the reflectance band-related downscaling methods, STARFM had the best performance, followed by ESTARFM and Regression, respectively.
- Among the thermal band-related downscaling methods, the SADFAT algorithm performed best, followed by Cokriging and Regression.
- The use of STARFM algorithm for reflectance bands and SADFAT algorithm for thermal bands in homogeneous areas is recommended.
- The difference between the different combinations of methods for estimating actual evapotranspiration is small.
Keywords: Downscaling; Landsat-8; MODIS; Evapotranspiration; Cokriging; STARFM
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