ArcGIS Geostatistical Analyst Research Articles

Geoinformation modeling of radioactive contamination of territories on the example of mines of the “ShidGSK” mining and processing plant

The aim of the study was to model the territorial distribution and statistical assessment of migration through the trophic chain of naturally occurring radionuclides released into the environment as a result of uranium ore mining. Statistical, geoinformation and experimental research methods were used in the study. Interpolation of the results of volumetric activity of natural radionuclides in environmental components and development of spatial models of their territorial distribution were performed using the ArcGIS software; statistical processing of modelling results and development of mathematical models of migration of natural radionuclides between environmental components were performed using ArcGIS Geostatistical Analyst software. The paper substantiates the choice of the method of geostatistical modelling of the territorial distribution of volumetric activity of natural radionuclides 234U, 238U, 210Po, 210Pb, 226Ra in soils and plants, which allows modelling the values of probabilistic indicators of radioecological contamination in the absence of a sufficient array of initial actual research results. Based on the analysis of the data obtained, the methodology of mathematical modelling of migration of natural radionuclides between soil and plant parts was further developed, which will facilitate consideration of the specifics of migration of natural radionuclides through the trophic chain and help in determining the level of radioecological hazard to the environment. The developed territorial models allow one to obtain stochastic data for their further analytical processing and visualizing radioecological hazard zones. Based on the developed models, zones of increased radioecological hazard within the existing sources of radioactive contamination were identified.

Analysis of Leachate from the Municipal Solid Waste Disposal Site and Its Impact on Ground Water Quality At, Lucknow Cantonment

Abstract: Open dumping of municipal solid waste is prone to groundwater contamination because of leachate production. In this report, an attempt has been made to assess the pollution potential of leachate generated from the solid waste disposal site and its impact on aquifers in Neil line Lucknow environs, Uttar Pradesh, India. Physico-chemical parameters of leachate are determined to evaluate water quality index (WQI) .A large amount of the water requirement of Lucknow City is supplied from groundwater. The quality of this groundwater was determined by taking samples from wells within the study area. The purposes of this investigation were .To provide an overview of present groundwater quality . To determine spatial distribution of groundwater quality parameters such as pH, electrical conductivity, Cl− , SO4 −2 , hardness, and NO3− concentrations, and.To map groundwater quality in the study area by using GIS and Geostatistics techniques. ArcGIS 9.0 and ArcGIS Geostatistical Analyst is used for generation of various thematic maps and ArcGIS Spatial analyst to produce the final groundwater quality map .

Analysis of Leachate from the Municipal Solid Waste Disposal Site and Its Impact on Ground Water Quality at Lucknow Cantonment

Abstract: Open dumping of municipal solid waste is prone to groundwater contamination because of leachate production. In this report, an attempt has been made to assess the pollution potential of leachate generated from the solid waste disposal site and its impact on aquifers in Neil line Lucknow environs, Uttar Pradesh, India. Physico-chemical parameters of leachate are determined to evaluate leachate pollution index (LPI) .A large amount of the water requirement of Lucknow City is supplied from groundwater. The quality of this groundwater was determined by taking samples from wells within the study area. The purposes of this investigation were to provide an overview of present groundwater quality. To determine spatial distribution of groundwater quality parameters such as pH, electrical conductivity, Cl− , SO4 −2 , hardness, and NO3− concentrations, and To map groundwater quality in the study area by using GIS and Geostatistics techniques. ArcGIS 9.0 and ArcGIS Geostatistical Analyst is used for generation of various thematic maps and ArcGIS Spatial analyst to produce the final groundwater quality map .

Relationships between habitat quality and ecological properties across Ziarat Basin in northern Iran

This study aimed to examine the relationships between habitat quality and ecological properties including land use, elevation, slope, and soil parameters (SOC, phosphorus, and color parameters in the standard CIE L*a*b* color system), across the Ziarat Basin of the Gharehsoo River in northern Iran. Data from satellite imagery, field sampling, and previous reports were used to quantify habitat quality using InVEST, while soil properties were mapped using the IDW method. The relationships between all criteria were assessed using the ArcGIS Geostatistical Analyst and Pearson’s correlation coefficient. The impact of land use on habitat quality was also evaluated based on a subset of landscape metrics including NP, PD, LPI, LSI, and PCI. The results demonstrated that the southern habitats had higher quality than the northern parts of the basin. Habitat quality had a significant positive relationship with elevation (R = 0.9), slope (R = 0.77), SOC (R = 0.65), and a* parameter (R = 0.57), whereas it had a significant inverse relationship with phosphorus (R = − 0.61) and L* parameter (R = − 0.84). Moreover, elevation and slope had a significant positive correlation with SOC (R > 0.53) and a* parameter (R > 0.47), and a significant negative correlation with phosphorus (R < − 0.42) and L* parameter (R < − 0.76). The analysis of landscape metrics also revealed that an enhance in the number of rangeland and forest patches increase habitat quality, whereas increasing agricultural and built-up land uses downgrade habitat quality. Conclusively, habitat quality can be correlated to landform and soil properties influenced by spatial patterns and land use types, which facilitate the understanding of ecological characteristics and land degradation.

Application of Remote Sensing and Geo Statistical Analysis for Soil Salinity Monitoring in Tina Plain Area of Egypt

In Egypt require to improve agricultural production to save food demand with increase population. Soil salinity is a significant attractive problem for agriculture in irrigated areas. As a result, monitoring of soil salinity is required for salinization control. One of the greatest challenges faced by Egypt’s decision-makers is the acquisition of reliable integrated soil salinity information to introduce and recommendation of a simple approach to formulating a guideline for stakeholders. Saline affected areas are vast, making it extremely difficult to examine soil quality with field and laboratory data to provide reliable approach to forecast and monitor soil salinity. Objective of this paper is to research remote sensing and ArcGIS tools by Geo-Statistical Analyst techniques to map saline-sodic heavy clay soils of the Tina plain area in Egypt. Tina Plain characterizes a serious area for potential development of agricultural land in Egypt. Satellite images were downloaded by Landsat 8, Sentinel-2A, Sentinel-1, and Synthetic Aperture Radar C-band data were wont to map soil salinity within the area. By coefficient of correlation method were preformed, evaluated and compared of the Three models. The results revealed that the Sentinel-2A optical imaging satellite yielded the very best prediction performance. ArcGIS Geo-Statistical Analyst was also successfully wont to predict and map the saline-sodic heavy clay soils with a mistake percentage of about 4.28%, which is taken into account as a minor error. generally, the study confirms that the Remote Sensing and ArcGIS Geo-Statistical Analyst are often considered by researchers and decision-makers as a credible, cost-effective, and time-controlled techniques to work out and predict the extension of soil salinity.

Use of neural networks and spatial interpolation to predict groundwater quality

The artificial neural networks share its working analogous with the human brain; and by using these artificial neural models, various complex nonlinear relationships can be modeled which cannot be described easily using mathematical equations. In this study, groundwater quality at a sanitary landfill site used for solid waste disposal was modeled using artificial neural networks. The groundwater quality was assessed for two consecutive years 2016 and 2017 at ten locations near the site, and the data were used for modeling. Total hardness was predicted using neural networks by using three learning algorithms, and the best one was used in the final model for prediction. The interpolation maps were drawn for both the years to understand the total hardness concentrations at unsampled locations using ArcGIS Geostatistical Analyst Extension, and Inverse Distance Weighing method was used. The percentage effect of spatial and temporal changes on total hardness was calculated by doing the sensitivity analysis and thus finding the relative importance of each input parameter on total hardness. Different algorithms were tested to select the best-performing algorithm with optimal neural architecture.

GEOSTATISTICAL ANALYSIS FOR LANDSLIDE PREDICTION IN TRANSILVANIA, ROMANIA

Abstract. Management of spatial data by means of Geographic Information System (GIS) plays an essential role based on the latest achievements in Geomatics domain. Geomatics offers the possibility to share, compare, and exchange data between researcher and users in unambiguous and accessible ways for map production and user-friendly technologies for results communication. Although sometimes the geodesist’s contribution to certain projects for landslide monitoring meant to develop early-warning-systems or risk maps is not adequately appreciated and he is only seen as supplier of measured geometric data, the geodesist has a significant contribution through his abilities regarding the modelling of dynamic systems, like strategic constructions (dams, tall buildings etc.) or landslides and data processing and interpretation. This study focuses on using geomorphological characteristics to detect the changes and the effects of landsliding using the ArcGIS 10.1 extension, Geostatistical Analyst. One of the main uses of geostatistics is to predict values of a sampled variable over the whole area of interest, which is referred to as spatial prediction or spatial interpolation. The extension allows creating a surface from data measurements occurring over an area where collecting information for every possible location would be impossible and gives us the possibility to fully understand the qualitative and quantitative aspects of the data. By providing us with the opportunity to predict and model spatial phenomena based on statistics and incorporating powerful exploration tools, ArcGIS Geostatistical Analyst effectively bridges the gap between geostatistics and geographic information system (GIS) analysis.

Predicting the Seasonal NDVI Change by GIS Geostatistical Analyst and Study on Driver Factors of NDVI Change in Hainan Island, China

As Hainan Island belonged to tropical monsoon influenced region, vegetation coverage was high. It is accessible to acquire the vegetation index information from remote sensing images, but predicting the average vegetation index in spatial distributing trend is not available. Under the condition that the average vegetation index values of observed stations in different seasons were known, it was possible to qualify the vegetation index values in study area and predict the NDVI (Normal Different Vegetation Index) change trend. In order to learn the variance trend of NDVI and the relationships between NDVI and temperature, precipitation, and land cover in Hainan Island, in this paper, the average seasonal NDVI values of 18 representative stations in Hainan Island were derived by a standard 10-day composite NDVI generated from MODIS imagery. ArcGIS Geostatistical Analyst was applied to predict the seasonal NDVI change trend by the Kriging method in Hainan Island. The correlation of temperature, precipitation, and land cover with NDVI change was analyzed by correlation analysis method. The results showed that the Kriging method of ARCGIS Geostatistical Analyst was a good way to predict the NDVI change trend. Temperature has the primary influence on NDVI, followed by precipitation and land-cover in Hainan Island.

Spatial Distribution Analysis and Mapping of Groundwater Quality Parameters for the Sylhet City Corporation (SCC) Area Using GIS

As Groundwater is a natural source of drinking water, it needs to be monitored regularly and people should be made aware of its quality. The unscientific management and exploration of groundwater resources has always been a serious problem in many cities in Bangladesh. As a result the quality of groundwater has become equally important as of its quantity. The present study is aimed to assess the current condition of groundwater quality and to analyze the spatial distribution of groundwater quality for the Sylhet City Corporation (SCC) area. The groundwater quality parameters were analyzed for 51 samples collected from the existing shallow tube wells from the twelve wards of SCC area. Arc GIS geostatistical analyst extension module was used for exploratory data analysis, semivariogram model selection, cross validation. Experimental semivariogram values are examined to find out the best fitted ordinary kriging (OK) models for eleven water quality parameters: pH, potassium, total hardness, alkalinity, turbidity, calcium, total dissolve solids, sulfate, nitrate, chloride and iron. The values of prediction errors i.e. mean square error (MSE), root mean square error (RMSE), average standard error (ASE), root mean square standardize error (RMSSE) were considered to justify the best fitted model. The interpolated spatial maps of different groundwater parameters shows that iron, alkalinity, total hardness and turbidity are vulnerable to groundwater quality within the study area.

Geostatistical analysis for predicting soil biological maps under different scenarios of land use

The ArcGIS Geostatistical Analyst aims to effectively bridge the gap between geostatistics and geographical information system analysis by enabling to model spatial phenomena and accurately predicting values within the study area. This approach was conducted to forecast the distribution patterns of some soil biological indices in Mirabad area, North West of Iran. Three different land uses (apple orchard, crop production, and rich pasture) were selected to conduct the experiments in a randomized completely blocks design with five blocks. Soil samples (0–30 cm) were collected on mid July 2010. Soil biological indices i.e. (i) substrate induced respiration, (ii) microbial biomass carbon, (iii) the activity of urease; (iv) alkaline phosphomonoesterase, and also (v) dehydrogenase were determined. Kriging and inverse distance weighting methods were applied to assess the spatial variability of five stated indices. Ordinary kriging was applied because it is the most general and widely used method. Digital soil biological indices maps will be the last output of integrating geostatistics and geographical information system. The study, while addressing spatial variability of soil biological properties, also discusses the accuracy of modeling as well as spherical model is now distinguished as the best fitted model. Assessing spatial variability of alkaline phosphomonoesterase activity has the lowest accuracy than urease and dehydrogenase activities. The geostatistical results showed that management practices might not have relevant effect on microbial biomass carbon and enzyme activities. But, the statistical analysis revealed significant differences between pasture and two other land uses.

Spatial and temporal mapping of groundwater salinity using ordinary kriging and indicator kriging: The case of Bafra Plain, Turkey

Groundwater salinity contributes significantly to soil salinization in irrigated areas. In this study, spatial and temporal analyses of groundwater salinity were performed based on data from 97 wells monitored over a 7-year period. ArcGIS Geostatistical Analyst was used in exploratory data analysis, semivariogram model selection, cross-validation and development of a groundwater salinity distribution pattern. Groundwater salinity semivariogram models varied by year and included exponential (2004, 2009), spherical (2005), J-Bessel (2006) and rational (2007, 2008, 2010) models. Ordinary Kriging (OK) was used to analyze spatial variability of groundwater salinity, whereas Indicator Kriging (IK) was used to analyze groundwater salinity in relation to pollution threshold values.Spatial variability maps show a decrease in groundwater salinity from 2004 to 2010, with salinity levels in 31% of the study area exceeding 5.0dSm−1 in 2004, compared to 9% of the study area in 2010. Moreover, probability maps show that 13.60% of the total area had the highest probability (0.8–1.0) of exceeding the threshold in 2004, compared to 0% in 2010.

Geostatistical calculation for clay reserve in Azraq Basin in Jordan

Industrial minerals in the Hashemite Kingdom of Jordan are vital to both economical and industrial development and this is true as far as the estimation of the reserves is carried out appropriately. The aim of this study was to geostatistically investigate the clay spatial behavior and geological reserve in Qa' Al-Azraq area in north east Jordan through known seventy four random drilled boreholes. Quantification of spatial arrangement or dependence was investigated through defining the best variogram model using optimal unbiased geostatistical tools of ArcGIS-Geostatistical Analyst, and MINEX. Clay thickness kriged map indicates a central concentration distribution of clay. Variations in kriged map tones comply with the major local fault systems allocated at the center of the study area forming pond structure bounded by the four faults. Two east-west cross sections were implemented using MINEX software. They provide visual maps for clay thickness distribution along the impurities of diatomite and interburden layers of silt, sand and gypsum. Clay reserves estimated using Inverse Distance Method (IDM) and Ordinary Kriging Method (OKM) were 6.1 and 5.94 Billion Cubic Meter respectively. The results of this study indicate that the area is a promising future target for economic investment in the local raw materials. Key words: Ore reserve, ordinary kriging, arcmap, MINEX.

Groundwater quality mapping in urban groundwater using GIS

Konya City, located in the central part of Turkey, has grown and urbanized rapidly. A large amount of the water requirement of Konya City is supplied from groundwater. The quality of this groundwater was determined by taking samples from 177 of the wells within the study area. The purposes of this investigation were (1) to provide an overview of present groundwater quality and (2) to determine spatial distribution of groundwater quality parameters such as pH, electrical conductivity, Cl-, SO4(-2), hardness, and NO3- concentrations, and (3) to map groundwater quality in the study area by using GIS and Geostatistics techniques. ArcGIS 9.0 and ArcGIS Geostatistical Analyst were used for generation of various thematic maps and ArcGIS Spatial Analyst to produce the final groundwater quality map. An interpolation technique, ordinary kriging, was used to obtain the spatial distribution of groundwater quality parameters. The final map shows that the southwest of the city has optimum groundwater quality, and, in general, the groundwater quality decreases south to north of the city; 5.03% (21.51 km2) of the total study area is classified to be at the optimum groundwater quality level.

Impacts of Climate Change on Regional Hydrological Regimes in the Spokane River Watershed

This study develops and implements a methodology to estimate the impacts of global climate change on regional hydrological regimes using ArcGIS Geostatistical Analyst. The model is easily used and can be expanded to different watersheds. The ArcGIS Geostatistical Analyst interface provides a comprehensive set of tools for creating surfaces from measured sample points compared with the previous method of using adjustable tension continuous curvature surface gridding. As a result, users can rapidly compare different interpolation techniques in order to obtain the best solution. Model results can subsequently be used in GIS models for visualization and analyses. The methodology was applied to the Spokane River Watershed. Results indicate that a 30% precipitation increase causes a 50% increase of streamflow when the temperature is normal compared to only a 20–30% increase in streamflow if the average annual air temperature is 1.5°C higher than normal. Conversely, a 20% precipitation decrease results in approx...

Impacts of climate variability on stream‐flow in the Yellow River

AbstractThis paper examines the impacts of climate variability upon the regional hydrological regimes of the Yellow River in China. Results indicate that the average annual precipitation is 494·8 mm in La Niña years and only 408·8 mm in El Niño years. The difference is 86·0 mm, or 18·8% over the long‐term average. The stream‐flows in the La Niña years are higher than that in El Niño years: 9·2% at the Lan‐Zhou station, 9·5% for Tou‐Dao‐Guai station, 11·8% for Long‐Men, 17·6% for San‐Men‐Xia, 19·2% at the Hua‐Yuan‐Hou station, and 22·0% at the Li‐Jin station. Both precipitation and stream‐flow responses show temporal and spatial patterns. The relationship among the stream‐flow, precipitation, and temperature, which was obtained by ArcGIS Geostatistical Analyst based on observed data, indicates stream‐flow is sensitive to both precipitation and temperature. For small precipitation increases (less than 13%), the stream‐flow percentage change is less than the precipitation change for the Yellow River. The results of this paper can be used as a reference for watershed water resources planning and management to maintain the healthy life and proper function of the river. Copyright © 2007 John Wiley &amp; Sons, Ltd.

Application of the ESRI Geostatistical Analyst for determining the adequacy and sample size requirements of ozone distribution models in the Carpathian and Sierra Nevada Mountains.

Models of O3 distribution in two mountain ranges, the Carpathians in Central Europe and the Sierra Nevada in California were constructed using ArcGIS Geostatistical Analyst extension (ESRI, Redlands, CA) using kriging and cokriging methods. The adequacy of the spatially interpolated ozone (O3) concentrations and sample size requirements for ozone passive samplers was also examined. In case of the Carpathian Mountains, only a general surface of O3 distribution could be obtained, partially due to a weak correlation between O3 concentration and elevation, and partially due to small numbers of unevenly distributed sample sites. In the Sierra Nevada Mountains, the O3 monitoring network was much denser and more evenly distributed, and additional climatologic information was available. As a result the estimated surfaces were more precise and reliable than those created for the Carpathians. The final maps of O3 concentrations for Sierra Nevada were derived from cokriging algorithm based on two secondary variables — elevation and maximum temperature as well as the determined geographic trend. Evenly distributed and sufficient numbers of sample points are a key factor for model accuracy and reliability.