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Sharpening filters are used to highlight fine image details, including object edges. However, sharpening filters are very specific to different types of images as they may create undesired edge effects, over-highlight fine details, or emphasize noise. Laplacian, Laplacian of Gaussian, high-boost, unsharp masking filters, and their extended algorithms are among most widely used sharpening spatial filters. This paper introduces a method that integrates anisotropic averaging with the Laplacian kernels for grayscale image sharpening. The proposed methodology is based on the concept of kriging computation in geostatistics for determining optimal interpolation weights in spatial domain. The convolution of kriging and Laplacian kernels is then carried out for image sharpening. Experimental results suggest certain advantages of the proposed linear convolution model for image sharpening over the Laplacian, Laplacian of Gaussian, high-boost, unsharp masking, and anisotropic diffusion methods in terms of the balance of sharpness and natural visualization. Another advantage of the proposed method is that it does not require any input statistical parameters.

A variogram-based two-point geostatistical approach was applied to generate a geological model of a petroleum reservoir. The geology consists of a sandstone formation with uniformly inclined rock strata of equal dip angle structurally trapped by surrounding faults. Data exploration of electrical well logs using univariate/bivariate statistical tests and data transformation tools demonstrated the data to be statistically suitable for ordinary kriging and sequential Gaussian simulation. Three directions were defined as part of the variogram and the data were interpolated resulting in a 3D subsurface representation. Validation included performing a leave-one-out cross-validation for each well and statistical comparison of multiple realizations generated from a computed stochastic model. The results display a reliable geological model which indicate a direct causation of the continuity trends from the bedding attitude of the regional fault trap.

Landslides triggered by heavy rainfall pose significant threats to human settlements and infrastructure in temperate and equatorial climate regions. This study focuses on the development of the Open Landslide Project (OLP), an open source landslide inventory aimed at facilitating geostatistical analyses and landslide risk management. Using a multidisciplinary approach and open source, multisatellite imagery data, more than 3000 landslides triggered by the extreme rainfall of autumn 2019 in northwestern Italy were systematically mapped. The inventory creation process followed well-defined criteria and underwent rigorous validation to ensure accuracy and reliability. The dataset’s suitability was confirmed through multivariate correlation and Double Pareto probably density function. The OLP inventory effectiveness in assessing landslide risks was proved by the development of a landslide susceptibility model using binary logistic regression. The analysis of rainfall and lithology revealed that regions with lower rainfall levels experienced a higher occurrence of landslides compared to areas with higher peak rainfall. This was attributed to the response of the lithological composition to rainfalls. The findings of this research contribute to the understanding and management of landslide risks in anthropized climate regions. The OLP has proven to be a valuable resource for future geostatistical analysis.

Environmental parameters such as air temperature (T) and air relative humidity (RH) should be intensively monitored in a greenhouse in real time. In most cases, one set of sensors is installed in the centre of a greenhouse. However, as the microclimate of a greenhouse is always heterogeneous, the sensor installation location is crucial for practical cultivation. In this study, the T and RH monitoring performance of different sensors were compared. Two types of real-time environmental sensors (Air Temperature and Humidity sensor and Activity Monitoring sensor, referred as ATH and AM) were selected and calibrated by reliable non-real-time sensors (Honest Observer By Onset sensor, referred as HOBO). The results showed that T and RH were variable in a small greenhouse area (128 m²). ATH had better T and RH monitoring performance than AM using HOBO as a reference (R² = 0.968 and 0.938 for T; 0.594 and 0.538 for RH, respectively, for ATH and AM). In terms of cost, it is more efficient to use more sets of AMs (15 sets were used in this case study) to establish an intensive monitoring system based on the Internet of Things (IoT) compared with that of ATH. Then, the optimal sensor installation location was decided using geostatistics. Based on a simulated monitoring data set, the optimal sensor installation location was determined to be 1.57 m away from the physical centre of the monitoring area. By combining IoT with geostatistics, this study offers a method for effective monitoring of environmental parameters in a practical greenhouse system with a three-step procedure. [ABSTRACT FROM AUTHOR]

Karacaören-II Dam Lake is a relatively deep reservoir located in Burdur, Turkey. It is one of the most important natural recreational and culture fishing area in the region. Although the lake has been constructed for irrigation and supply energy in the past, it is considered as a drinking water supply for Antalya in near future. However, it was mentioned that the lake suffers from eutrophication problem in recent scientific studies. So, the evaluation of water quality in the lake with different geostatistical techniques will facilitate the work of the managers to determine precautions and improve the water quality. In this study, water quality measurements were performed at 22 sampling locations in Karacaören-II Dam Lake. Field works were carried out in November 2015 and August 2016. Firstly, spatial distribution maps of SD, DO and NO3-N were constituted using non-statistical Inverse Distance Weighted (IDW) and the statistical Ordinary Kriging (OK) interpolation methods within geostatistical analyst tool of ArcGIS program. Then, seasonal changes of the distributions were evaluated. Error metrics obtained from cross-validation was used to assess correctness of the interpolation methods. The results showed that both methods were effective in the identification of problematic zones. Higher SD and relatively lower DO values were clustered near outlet of the lake. DO was more sensitive to microscale variations. Although different methods were superior when different parameters, error metrics and seasons considered, most of the time OK method outperformed IDW. [ABSTRACT FROM AUTHOR]

Kriging has been shown to be the best interpolator to interpolate maps in precision agriculture. However, Kriging requires a high number of sampling points to generate accurate maps. Recently, machine learning (ML) techniques have shown the potential to produce maps with a lower number of sampling points. In addition, using ML map generation can be automated and use much more feature information to improve map quality. Therefore, the objective of this study was to implement a ML technique and compare it to IDW and to Ordinary Kriging (OK). The ML algorithm used was the Support Vector Machine (SVM). Software based on the SVM method was developed (Smart-Map) using the Python language. This software was tested in an area of 204 ha cultivated with soybeans. The performance of the SVM method was compared to traditional interpolation methods, IDW and Ordinary Kriging (OK). Based on the analysis of 10 soil attributes, OK had better performance than IDW and the ML method when the Moran's I (Index) values were significant and higher than 0.67. With a low density of points and low degrees of spatial autocorrelation, the ML method performed better than IDW and OK. [ABSTRACT FROM AUTHOR]

This study assessed the health of the mangrove ecosystem and mapped the spatial variation in selected variables sampled across the Matang Mangrove Forest Reserve (MMFR) by using a geostatistical technique. A total of 556 samples were collected from 56 sampling points representing mangrove biotic and abiotic variables. All variables were used to generate the semivariogram model. The predicted variables over the entire MMFR have an overall prediction accuracy of 85.16% (AGB), 90.78% (crab abundance), 97.3% (soil C), 99.91% (soil N), 89.23% (number of phytoplankton species), 95.62% (number of diatom species), 99.36% (DO), and 87.33% (turbidity). Via linear weight combination, the prediction map shows that mangrove ecosystem health in Kuala Trong throughout the Sungai Kerang is excellent (5: MQI > 1.5). Some landward areas of Kuala Trong were predicted to have moderate health (3: −0.5 ≤ MQI ≤ 0.5), while Kuala Sepetang was predicted to have the bad ecosystem health (2: −1.5 ≤ MQI ≤ −0.5), with active timber harvesting operations and anthropogenic activities in the landward areas. The results of this method can be utilised to carry out the preferred restoration, through appropriate management and facilities distribution, for improving the ecosystem health of mangroves. [ABSTRACT FROM AUTHOR]

Underground mining at Obuasi in Ghana has been in operation since 1947. This paper uses geostatistical methods to evaluate gold ore blocks to ensure reliable grades for mining large tonnage and low-grade resources. Historically, the principal ores were low tonnage, high grade and relatively homogeneous quartz stockwork with simple geometry and average bulk grades in the range of 20-30 g/t that were evaluated using conventional polygonal methods and mined by semi-mechanized means. Currently, the ore is a shear-hosted mixed quartz vein and disseminated sulphide type deposit of low grade that is mined using highly mechanized means. The need therefore arises for a re-assessment of the estimation procedures to ensure prolonged and more profitable mining. Both diamond drill (DD) core and stope/cross-cut channel samples were taken from Block 1 at the mine for analyses and re-assessment. A wireframe model was used to constrain the three dimensional (3D) block model of the deposit. Ordinary kriging (OK) and multiple indicator kriging (MIK) geostatistical methods were used to estimate gold grades. Grade distribution is positively skewed with high spatial variability and extreme values while background values are established as

Spodoptera exigua (Hübner) is a destructive insect pest of sugar beet globally. In this study, we utilized some geostatistical techniques to locate sample points and determine spatial distribution pattern of the pest. Pheromone traps were used to predict accurate time of oviposition and visual sampling was used to determine larval density. In order to predict the population density at non-sampled locations, Inverse Distance Weighting (IDW) and Ordinary Kriging (OK) techniques were used. The densities of different developmental stages of S. exigua were estimated weekly during August and September, 2016. The degree dependence values of S. exigua eggs in majority of dataset were larger than 76%, indicating their clumped distribution and strong spatial autocorrelation. Broadcast spraying was used when the density of the larvae in tracts overpassed the economic threshold. Also, we applied site-specific spraying to grid cells when larval densities were above the economic threshold. In most cases, larval mortality was not significantly different between broadcast spraying and site-specific spraying methods. Comparing two interpolation methods indicated that the data calculated for density of larvae and eggs of S. exigua were fitted better with OK model than IDW one. The findings of this research would be useful to develop sampling programs and to control S. exigua in Iran.

This paper deals with Kriging models applied to optimise braking performances for freight trains. More precisely, it is focused on mass distribution optimisation aimed at reducing the effects of in-train forces among vehicles, e.g. compression and tensile forces, in-train emergency braking. To this end, Kriging models are applied with covariance structure based on the Matérn function, introducing specific input parameters to better outline the payload distribution on the train, also evaluating the shape of the payload distribution. The different shapes, related to the payload distributions, have been implemented into a model through a Python routine, which has been used to ‘assemble’ the simulated trains. The analysed train carries 80% of its maximum payload capacity during an emergency braking from the speed of 30 km/h. Satisfactory results have been obtained considering compression forces, tensile forces and their sum, also considering residuals and diagnositc measures. [ABSTRACT FROM PUBLISHER]

Abstract Many developing countries experience widespread groundwater declination. Sustainable management actions include generation of an accurate groundwater distribution based on an extensive groundwater monitoring network which is often cost prohibiting in the context of a developing country such as Bangladesh. Further, such knowledge is lacking for the Sylhet region where groundwater was documented to be under tremendous pressure. Specifically, the gap in the current literature exists regarding groundwater trends and its areal extent for this region. This paper bridges the gap in research by focusing on trends and spatial and temporal variation of groundwater level changes for this area. This study addresses this problem by creating groundwater level predictions at the ungauged areas using geostatistical methods applied to a detailed set of data. In this study, the spatial variability of annual-average depth to the water table at 46 observation wells in the Sylhet division in Bangladesh is analyzed for 2000, 2005, 2010, and 2015. The geostatistical analysis applies the ordinary kriging method with cross-validation to create the water table maps for the study area. The results indicate a substantial increase in groundwater depths during the studied period from 2000 to 2015 in some locations in the study area. Importantly, this work identifies the vulnerable zones in the area due to the groundwater lowering trend. The study adds to the groundwater management research in developing countries and focuses on the spatial and temporal groundwater variation. The findings from the modeling exercise contribute to identification of the vulnerable areas and therefore help policymakers in making informed decisions to manage groundwater resources in this sensitive region sustainably.

Abstract An accurate analysis of spatial rainfall distribution is of great importance for managing watershed water resources, in addition to giving support to meteorological studies and agricultural planning. This work compares the performance of two interpolation methods: Inverse distance weighted (IDW) and Kriging, in the analysis of annual rainfall spatial distribution. We use annual rainfall data for the state of Rio Grande do Sul (Brazil) from 1961 to 2017. To determine which proportion of the sample results in more accurate rainfall distribution maps, we use a certain amount of points close to the estimated point. We use mean squared error (MSE), coefficient of determination (R 2), root mean squared error (RMSE) and modified Willmott's concordance index (md). We conduct random fields simulations study, and the performance of the geostatistics and classic methods for the exposed case was evaluated in terms of precision and accuracy obtained by Monte Carlo simulation to support the results. The results indicate that the co-ordinary Kriging interpolator showed better goodness of fit, assuming altitude as a covariate. We concluded that the geostatistical method of Kriging using nine closer points (50% of nearest neighbors) was the one that better represented annual rainfall spatial distribution in the state of Rio Grande do Sul.

The receptor model of Positive Matrix Factorization (PMF) is widely used to identify the air pollution sources of fine particulate matter (PM 2.5). However, using data collected at a single site in PMF analysis limits its ability to characterize the source emission regions. To recognize the hotspots of emission sources, this study incorporated spatially distributed PM 2.5 mass concentrations into PMF modeling to estimate the spatial distribution of source-specific PM 2.5. A local emission source of road dust/civil construction was retrieved and selected for evaluation of the characterized hotspot regions. The spatial distribution of the corresponding source-specific PM 2.5 contributions in the study area was compared with the land use features. The positive correlations (coefficients ≥0.40) were acquired between source-specific PM 2.5 and land use characteristics, such as major road length and the number of construction sites. A leave-one-out cross-validation R 2 of 0.48 was achieved using the land use regression model. These findings demonstrated the effectiveness of the proposed approach in capturing spatial variations of local emission sources. Future studies are suggested to expand the study area to further assess the extent of application of the PMF spatialization results. • Spatially distributed PM 2.5 were utilized in PMF to spatialize source contributions. • Hotspots of a PMF-retrieved local emission source were characterized. • Associations between source-specific PM 2.5 and land features were identified. [ABSTRACT FROM AUTHOR]

• An error decomposition and fusion framework is proposed for daily precipitation estimation. • Geographically weighted regression and geographical difference analysis is implemented. • Improved precipitation estimates are achieved by the merging product. • The merging product demonstrates its superiority in accurately estimating winter precipitation and high-value precipitation. • The influence of GPCC station data on the accuracy of the merging product has been considered. Near-real-time satellite precipitation products (SPPs) possess inherent application prospects in the hydro-meteorological field due to their convenient acquisition and accessibility. Integrating gauge-based measurements with near-real-time SPPs is an effective approach for achieving precise spatial precipitation estimates at daily scale. This study proposed a newly developed error decomposition and fusion framework, named EDGWR, which integrates error decomposition and geographically weighted regression (GWR). The final merged product, denoted as IMERG-EDGWR, was obtained from the near-real-time Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Early Run (IMERG-E) product. IMERG-EDGWR was compared with the raw IMERG-E, near-real-time IMERG-L, post-real-time IMERG-F, global multi-source merged precipitation data (MSWEP), interpolated results (IDW and OK), and direct application of GWR outcomes (IMERG-GWR), utilizing the daily ground measurements from 12 meteorological stations located in the Yellow River source region (YRSR). The evaluation results from 2014 to 2018 revealed that IMERG-EDGWR exhibited significant enhancements over the raw IMERG-E, surpassed the research-level IMERG-F, and generally outperformed IDW, OK, MSWEP, and IMERG-GWR. Notably, IMERG-EDGWR enhances the detection of heavy precipitation events, refining estimates of both magnitude and frequency for precipitation over 25 mm. During the winter season, IMERG-EDGWR produced the most accurate precipitation estimates with notable improvement of precipitation detection capabilities. An experiment reducing input data by excluding gauge observations from the GPCC dataset tested the robustness of the EDGWR algorithm, confirming its superiority even with diminished input data. The merging framework proposed in this study constitutes an efficacious and implementable solution to enhance the accuracy of near-real-time SPPs and is expected to be implemented in different regions in future research. [ABSTRACT FROM AUTHOR]

In this study, a Kriging-based metamodel is proposed that can be used instead of the simulation model for complex problems where data generation with a simulation model may be costly. In this new model structure, which is proposed for cases where the drift function structure of the Universal Kriging meta-model is not known. A power function of the variables that can also take fractional values is used instead of the first and second order regression models used as the drift function in the Universal Kriging metamodel. The predictive power of this metamodel, which is called Fractional Universal Kriging metamodel, has been investigated by experimentally computational analysis. Validation analysis reveals that the Fractional Universal Kriging metamodels have superior predictive power with respect to Mean Squared Error and Maximum Squared Error performance measures. Thus, in the case that the input-output relationship of the simulation model can be expressed with a power function that includes the effects of higher order and different from the quadratic polynomial case, Fractional Universal Kriging metamodels are proposed as a new metamodel approach. [ABSTRACT FROM AUTHOR]

The internal air temperature of Chinese solar greenhouse (CSG) has the problem of uneven spatial and temporal distribution. To determine temperature distribution at different locations, we designed a greenhouse temperature real-time monitoring system based on virtual local area network (VLAN) and estimate, including interpolation estimation module, data acquisition, and transmission module. The temperature data were obtained from 24 sensors, and the Ordinary Kriging algorithm estimated the temperature distribution of the whole plane according to the data. The results showed that the real-time temperature distribution monitoring method established was fast and robust. In addition, data validity rate for VLAN technology deployed for data transmission was 2.64% higher than that of cellular network technology. The following results are obtained by interpolation estimation of temperature data using gaussian model. The average relative error (ARE) of estimate, mean absolute error (MAE), root mean square error (RMSE), and determination coefficient (R2) were −0.12 °C, 0.42 °C, 0.56 °C, and 0.9964, respectively. After simple optimization of the number of sensors, the following conclusions are drawn. When the number of sensors were decreased to 12~16, MAE, RMSE, and R2 were 0.40~0.60 °C, 0.60~0.80 °C, and >0.99, respectively. Furthermore, temperature distribution in the greenhouse varied in the east–west and north–south directions and had strong regularity. The calculation speed of estimate interpolation algorithm was 50~150 ms, and greenhouse Temperature Distribution Real-time Monitoring System (TDRMS) realized simultaneous acquisition, processing, and fast estimate. [ABSTRACT FROM AUTHOR]

To understand the spatio-temporal dynamics and the effect of climate on fruit rot occurrence in arecanut plantations, we evaluated the intensity of fruit rot in three major growing regions of Karnataka, India for two consecutive years (2018 and 2019). A total of 27 sampling sites from the selected regions were monitored and the percentage disease intensity (PDI) was assessed on 50 randomly selected palms. Spatial interpolation technique, ordinary kriging (OK) was employed to predict the disease occurrence at unsampled locations. OK resulted in aggregated spatial maps, where the disease intensity was substantial (40.25–72.45%) at sampling sites of the Malnad and coastal regions. Further, Moran's I spatial autocorrelation test confirmed the presence of significant spatial clusters (p ≤ 0.01) across the regions studied. Temporal analysis indicated the initiation of disease on different weeks dependent on the sampling sites and evaluated years with significant variation in PDI, which ranged from 9.25% to 72.45%. The occurrence of disease over time revealed that the epidemic was initiated early in the season (July) at the Malnad and coastal regions in contrary to the Maidan region where the occurrence was delayed up to the end of the season (September). Correlations between environmental variables and PDI revealed that, the estimated temperature (T), relative humidity (RH) and total rainfall (TRF) significantly positively associated (p = 0.01) with disease occurrence. Regression model analysis revealed that the association between Tmax, RH1 and TRF with PDI statistically significant and the coefficients for the predictors Tmax, RH1 and TRF are 1.731, 1.330 and 0.541, respectively. The information generated in the present study will provide a scientific decision support system, to generate forecasting models and a better surveillance system to develop adequate strategies to curtail the fruit rot of arecanut. [ABSTRACT FROM AUTHOR]

Soil fertility and plant nutrition management have received much attention in order to increase efficiency, improve plant productivity and also reduce environmental hazards. Soil cation exchange capacity (CEC) is a critical indicator of soil fertility quality and pollutant separation capacity. Plants such as rice need to provide their nutrients using fertilisers for maximum production, so it is essential to know the content of the major elements such as nitrogen, phosphorus and potassium in the soil and to prepare their ideal maps. To evaluate the factors affecting soil fertility and preparation of their maps, a total of 255 soil samples were collected from the study area located in northern Iran. The soil CEC, total nitrogen, available potassium and phosphorus were analysed using standard methods. Spatial variability maps of soil fertility properties were prepared using Ordinary Kriging method. The results obtained from field data show that the amounts of total nitrogen, available potassium and phosphorus are more than optimal limit in many parts of the study area. Evaluation criteria values including normalised root mean square error (NRMSE), normalised mean absolute error (NMAE) and coefficient of determination (R2) were derived for CEC 0.164, 0.135 and 77.65, total nitrogen 0.232, 0.298 and 71.59, available phosphorus 0.214, 0.215 and 70.11 and available potassium 0.197, 0.146 and 78.15, respectively, in the test data group. NRMSE, NMAE and R2 values showed that the accuracy of prepared maps was ideal. The spatial distribution maps can be used as an appropriate guide for precise and specific management of NPK nutrients application to protect the environment and stop the contamination of groundwater resources within the study area. [ABSTRACT FROM AUTHOR]

Artificial neural networks are powerful global approximators for mineral grade assessment. The techniques are capable of retaining nonlinearity and spatial heterogeneity of a feature variable and can even perform modelling with noisy and incomplete data. These qualifiers make neural networks strong contenders for mineral grade assessment. In the present research, the authors have proposed a hybrid model consisting of two unsupervised models, namely ordinary kriging (OK) and k-means clustering, used as pre-processing steps to feed data to supervised generalized regression neural network (GRNN) model. While OK models the spatial variability and provides estimation on local scale, k-means clustering has been used for dimensionality reduction. These steps support in achieving accuracy and speed of the modelling process. GRNN model prepares its training and validation datasets using the k-clusters. Testing and validation of the model have been carried out on five live iron-ore deposits. Once the validation is found adequate and acceptable generalization with validation dataset is achieved, the model is verified with testing dataset. Deposit-wise value of R2 of the hybrid GRNN model has been found to vary between 0.93 and 0.99. The model is observed to deliver improved performance when compared with multi-layer perceptron, radial basis function and recurrent neural network models. Spatially distributed estimation maps generated retain nonlinearity and spatial heterogeneity of the original Fe data values. Thus, the hybrid GRNN model provides an edge over the standalone classical geostatistics or standalone GRNN model. [ABSTRACT FROM AUTHOR]

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Spatial variation of soil pH is important for the evaluation of environmental quality. A reasonable number of sampling points has an important meaning for accurate quantitative expression on spatial distribution of soil pH and resource savings. Based on the grid distribution point method, 908, 797, 700, 594, 499, 398, 299, 200, 149, 100, 75 and 50 sampling points, which were randomly selected from 908 sampling points, constituted 12 sample sets. Semi-variance structure analysis was carried out for different point sets, and ordinary Kriging was used for spatial prediction and accuracy verification, and the influence of different sampling points on spatial variation of soil pH was discussed. The results show that the pH value in Kenli County (China) was generally between 7.8 and 8.1, and the soil was alkaline. Semi-variance models fitted by different point sets could reflect the spatial structure characteristics of soil pH with accuracy. With a decrease in the number of sampling points, the Sill value of sample set increased, and the spatial autocorrelation gradually weakened. Considering the prediction accuracy, spatial distribution and investigation cost, a number of sampling points greater than or equal to 150 could satisfy the spatial variation expression of soil pH at the county level in the Yellow River Delta. This is equivalent to taking at least 107 sampling points per 1000 km2. The results in this study are applicable to areas with similar environmental and soil conditions as the Yellow River Delta, and have reference significance for these areas. [ABSTRACT FROM AUTHOR]

Soil organic matter (SOM) plays a crucial role in promoting soil tillage, improving soil fertility and providing crop nutrients. Investigation and sampling are the premise and basis for understanding the spatial distribution of SOM. The number of sampling points will affect the accuracy of spatial variation of SOM. Therefore, it is important scientific work to determine a reasonable number of sampling points under the premise of ensuring accuracy. In this study, Kenli County, a typical area of the Yellow River Delta in China, was taken as an example to investigate the effect of different sampling points on spatial-variation expression of SOM. A total of 12 sample subsets (including 900 samples) were randomly sampled at equal intervals from the 900 sample points, using geographic information system (GIS) technology and geostatistical analyses to explore the optimal number of samples. The results showed that the SOM content in the study area had a lower-middle degree of variation. As the number of sample points decreased, the spatial distribution of SOM showed the gradual weakening of detail-characterization ability; and when the number of sample points was too small (<100), there was a wrong expression that was not consistent with the actual situation. The value of RMSE has no obvious regularity with the change of sample number. The values of both ME and ASE showed a significant inflection point when the number of samples was 150 and remained around 0 and 4 as the number of samples increased, respectively. Combined with the three indicators of ME, RMSE and ASE, collecting at least 150 samples can satisfy the spatial-variation expression of SOM, equivalent to 107 sample points within the area of 1000 km2. The research results could provide important references for investigation of SOM content in areas with similar natural geographical conditions. [ABSTRACT FROM AUTHOR]

For modelling and predicting soil indicators to be fully operational and facilitate decision-making at any spatial level, there is a requirement for precise spatially referenced soil information to be available as input data. This paper focuses on showing the capacity of Sentinal-2A(S2A) multispectral imaging to predict soil properties and provide geostatistical analysis (ordinary kriging) for mapping dry land soil fertility conditions (SOCs). Conditioned Latin hypercube sampling was used to select the representative sampling sites within the study area. To achieve the objectives of this work, 48 surface soil samples were collected from the western part of Matrouh Governorate, Egypt, and pH, soil organic matter (SOM), available nitrogen (N), phosphorus (P), and potassium (K) levels were analyzed. Multilinear regression (MLR) was used to model the relationship between image reflectance and laboratory analysis (of pH, SOM, N, P, and K in the soil), followed by mapping the predicted outputs using ordinary kriging. Model fitting was achieved by removing variables according to the confidence level (95%).Around 30% of the samples were randomly selected to verify the validity of the results. The randomly selected samples helped express the variety of the soil characteristics from the investigated area. The predicted values of pH, SOM, N, P, and K performed well, with R2 values of 0.6, 0.7, 0.55, 0.6, and 0.92 achieved for pH, SOM, N, P, and K, respectively. The results from the ArcGIS model builder indicated a descending fertility order within the study area of: 70% low fertility, 22% moderate fertility, 3% very low fertility, and 5% reference terms. This work evidence that which can be predicted from S2A images and provides a reference for soil fertility monitoring in drylands. Additionally, this model can be easily applied to environmental conditions similar to those of the studied area. [ABSTRACT FROM AUTHOR]

Groundwater levels serve as a monitoring parameter of changes in groundwater storage and vulnerability status of coastal aquifers to seawater intrusion. Secondary piezometric levels datasets obtained in dry and wet seasons of 2017 from 12 dedicated observation wells in Ghiss-Nekor coastal plain were used to conduct this study. The measured data are reliable; however, investigated sampling locations are irregularly distributed and clustered along the northern half of the plain. Our research examined the performance of two linear kriging methods: empirical Bayesian kriging (EBK) and ordinary kriging (OK), regarding interpolation of this scattered dataset. Cross-validation results for assessing the prediction accuracy approved the selection of EBK as the best-fit method. By adopting interpolated EBK-based estimates, accurate groundwater levels distribution maps were developed enabling the delineation of zones at the highest risk of seawater intrusion or occurrence of slight piezometric recovery. Besides yielding satisfactory outcome for small number of observations, EBK technique generates standard errors associated with the predicted values; the latter were included in our study as a criterion for selecting locations of high uncertainty of estimates and requiring an increase in the number of wells. By considering the results of the Densify Sampling Network tool and the highest priority indices, 26 new wells are proposed to be added to the investigated observation network, with the possibility of excluding wells of least priority. Usage of DSN tool and priority index, relying respectively on the standard error of prediction surface and cross-validation residuals, is deemed satisfactory as the mean standard error diminished by over 60% after placing the additional monitoring sites. [ABSTRACT FROM AUTHOR]

The issue of soil contamination is one of the most important subjects that interests decision-makers all over the world. It is also related to soil fertility and food security. The soils adjacent to the drains in Egypt suffer from increasing concentration of heavy metals, which negatively affects soil and crop quality. Precise spatial distribution maps of heavy metals are an essential key to mitigating the negative impacts on the ecosystem. Sixty random soil locations adjacent to the El-Moheet drainage were chosen on the west side of the Nile River, El-Minia governorate, Egypt. Six heavy metals (Cr, Co, Cu, Cd, Pb, and Zn) were selected to generate their spatial pattern maps using ordinary Kriging (OK). Principal component analysis (PCA) and contamination factors (CF) were applied to evaluate soil contamination levels in the study area. The results showed that the Gaussiang model was a high fit for soil pH, and Pb, the Exponential model was fit for EC, Stable model was fit for OC, Co, Cu, and Cd. In addition, the Spherical model was fit for both Cr and Zn. The MSE values were close to zero in all selected metals, while the values of RMSSE were close to one. The results showed that the soil heavy metal concentrations were grouped into two clusters using PCA. Furthermore, three contamination degrees were obtained (moderate, considerable, and very high), with about 70.7% of the study area characterized by considerable heavy metals concentration, where the average heavy metals concentration (mg kg−1) in this degree was 91.23 ± 19.5, 29.44 ± 5.2, 53.83 ± 10.2, 1.12 ± 0.3, 36.04 ± 18.0, and 101.29 ± 35 for Cr, Co, Cu, Cd, Pb, and Zn, respectively. The current results reflect the mismanagement and use of low-quality water for irrigation in the study area, which increased the toxic element concentration in soil surface layers. In the end, the results of spatial distribution maps of pollutants and their degrees could support decision-makers as a basis for developing appropriate mitigation plans for heavy metals. [ABSTRACT FROM AUTHOR]

Mangrove forests are continuously degraded due to human activities, despite being a very valuable natural resource. Therefore, this study aimed to map the distribution and analyze mangrove forests' health based on the normalized difference vegetation index (NDVI) and environmental quality. The health distribution was determined through the processing of Sentinel-2A satellite imagery in 2020 and field measurements. The environmental quality was obtained by processing physical parameters including water temperature, salinity, pH, and substrate texture using the Ordinary Kriging method. Based on the results, Taman Hutan Raya (Tahura) Ngurah Rai mangrove forest is dominated by healthy trees which become worse once closer to the shore and riverbanks. Hence, mangrove vegetation with good conditions tends to have optimal environmental quality conditions and vice versa. [ABSTRACT FROM AUTHOR]

Total nitrogen (TN), total phosphorus (TP), total potassium (TK), and soil organic matter (OM) can significantly affect forest growth. However, these soil properties are spatially heterogeneously distributed, complicating the prescription of forest management strategies. Thus, it is imperative to obtain an in-depth understanding of the spatial distribution of soil properties. In this study, soils were sampled at 181 locations in the Tropical Forest Research Center in the southwestern Guangxi Zhuang Autonomous Region in southern China. We investigated the spatial variability of soil OM, TN, TP, and TK using geostatistical analysis. The nugget to sill ratio indicated a strong spatial dependence of soil TN and a moderate spatial dependence of soil OM, TP, and TK, suggesting that TN was primarily controlled by intrinsic factors (e.g., soil texture, parent material, vegetation type, and topography), whereas soil OM, TP, and TK were controlled by intrinsic and extrinsic factors (e.g., cultivation practices, fertilization, and planting systems). Based on the spatial variability determined by the geostatistical analysis, we performed ordinary kriging to create thematic maps of soil TN, TP, TK, and OM. Model validation indicated that the thematic maps were reliable to inform forest management. [ABSTRACT FROM AUTHOR]

Monitoring workers’ exposure to occupational noise is essential, especially in industrial areas, to protect their health. Therefore, it is necessary to collect information on noise emitted by machines in industries. This research aims to map the noise from mechanized mineral ore industry using the kriging interpolation method, and ArcGIS 10.5.1 to spatially process and analyze data. The experimental calculation result of the semivariogram showed a 0.83 range value, with an essential parameter of 1.75 sill and a spherical total theoretical model. The result shows that the main machines with the highest power consumption and the Leq value are located in the southwest position of the sampled areas with a noise map-projected to assess the workers’ noise exposure level. In conclusion, the study found that the highest noise level was generated ranged from 88 to 97 dBA and contributed to the whole sound pressure level at certain positions.

Precipitation has a dominant role in agriculture, and a regular rain pattern is usually vital to agriculture; excessive or inadequate rainfall can be harmful. In this paper, an agricultural drought index is utilized to study the agricultural drought periods and analyze them with their intensities at various locations. Some nonhomogeneous Poisson models are also used to calculate the probability of agricultural droughts (number of times occurred) in a time interval of interest. It is to be assumed that the number of agricultural drought event occurrences is a Nonhomogeneous Poisson Process (NHPP) has a rate function, which depends on some parameters that must be estimated. Two cases of these functions are considered: the Weibull and linear intensity function. The Bayesian approach with Gibbs sampling under the Markov Chain Monte Carlo (MCMC) algorithm is used to estimate the parameters of these functions. The most appropriate fitted model is selected by using Deviance Information Criteria (DIC) and use that appropriately fitted model to calculate the accumulated events of agricultural drought in a time interval of interest at each location. Ordinary Kriging (OK) is used to regionalize the parameters and present its spatial behavior. The results based on the DIC indicate that the Power Law Process (PLP) performs better than the linear intensity function, NHPP model. The interpolated parameter values for the appropriate model, their patterns, and fluctuations for the study area are efficiently presented using contour maps. It is a novel and straightforward approach to assess the selected model parameter values used to predict the accumulated drought events at un-sampled locations. The proposed framework might also help to analyze other spatial variables of interest and can be used for climate-change study, ecosystem modeling, etc. The findings can also help to make decisions for sustainable environmental management in Pakistan.

Accurate spatial distribution information of rainfall is essential to rainfall‐induced hazard predictions and statistical interpolation methods may serve as a useful tool to produce a detailed distribution from coarse data sources. Although numerous comparison studies about different interpolation methods have been conducted on irregular rain‐gauge networks, there is a need to perform such work on the increasingly available gridded rainfall data. Carried out in the Emilia‐Romagna region (Italy) from 2008 to 2018, this study aims to examine accurate and appropriate interpolation methods to produce finer rainfall surface maps based on the 0.25° × 0.25° ERA5 gridded precipitation datasets. Five interpolation techniques, namely Thiessen polygons, Inverse Distance Weighting (IDW), Thin Plate Spline (TPS), Ordinary Kriging (OK) and ordinary Co‐Kriging (CoK), have been selected and compared at different time scales (annual, monthly and annual maximum daily precipitation). To assess the accuracy, the leave‐one‐out‐cross‐validation test was used by using the indexes of Bias, correlation coefficient, the Nash‐Sutcliffe Efficiency, Root Mean Square Error and the Kling‐Gupta Efficiency (KGE). Additionally, visual inspections are employed to evaluate the plausibility of the interpolated maps. Results show: (a) All five interpolation methods have certain capabilities to improve spatial resolution, but they fail on accuracy at the daily scale. The OK generally outperforms the other four methods, while TPS shows better performance through the visual inspection at the monthly scale; (b) Unlike in the case of the interpolation using conventional point‐based rain‐gauge data, the multivariate method CoK is inferior to the univariate ones and the p‐power of the IDW also differs and (c) Winter and spring results are better than those of summer and autumn. This study has provided useful guidance on choosing suitable rainfall interpolation methods for gridded datasets, which can be expanded in other regions and data sources to explore the generality of the conclusions. [ABSTRACT FROM AUTHOR]

Spatial interpolation methods usually differ in their underlying mathematical concepts. Each has inherent advantages and disadvantages, and choosing a method should be based on the type of data to be analyzed. This paper, therefore, compares and evaluates the performances of well-established interpolation techniques that can be used to estimate monthly rainfall in Thailand. The approaches analyzed include inverse distance weighting (IDW), inverse exponential weighting (IEW), multiple linear regression (MLR), artificial neural networks (ANN), and ordinary kriging (OK) methods. In addition, a search of the nearest stations has also been conducted for some of the aforementioned schemes. A k-fold cross-validation is exploited to assess the efficiency of each method. Results show that ANN might be the least desirable choice as it underperformed, with the remaining methods being roughly comparable. Considering both accuracy and computational flexibility, the IEW approach with a restricted number of neighboring stations is recommended in this study. [ABSTRACT FROM AUTHOR]

The assessment of groundwater irrigation using robust tools is essential for the sustenance of the agro-environment in arid and semi-arid regions. This study presents a reliable method consisting of a combination of multivariate analysis and geostatistical modeling to assess groundwater irrigation resources in the Western Middle Cheliff (Algeria). For this goal, mean data from 87 wells collected during April to July 2017 were used. The hierarchical cluster analysis (HCA) using the Q-mode approach revealed three distinct water types, with mineralization increasing from cluster 1 to cluster 3. The Principal Component Analysis (PCA) utilizing the Varimax method approach allowed the extraction of three main components: the first and second (PC1, PC2), revealing that the geogenic process, have influenced the hydrogeochemical composition of groundwater. The pollution induced by agriculture activities has been related to PC3. Based on the combination of multivariate analysis and geostatistical modeling, the distribution maps were created by interpolating the factor distribution values acquired in the study region using the ordinary kriging (OK) interpolation method. The findings revealed that both natural processes and man-made activities have a substantial impact on the quality of groundwater irrigation. Cluster mapping, another often used combining approach, has shown its effectiveness in assisting groundwater resource management. [ABSTRACT FROM AUTHOR]

Identification and classification of high-risk areas for the presence of Aedes aegypti is not an easy task. To develop suitable methods to identify this areas is an essential task that will increase the efficiency and effectiveness of control measures and to optimize the use of resources. The objectives of this study were to identify high- risk areas for the presence of Ae. aegypti using mosquito traps and household visits to identify breeding sites; to identify and validate aspects of the remote sensing images that could characterize these areas; to evaluate the relationship between this spatial risk classification and the occurrence of Ae. aegypti; and provide a methodology to the health and control vector services and prioritize these areas for development of control measure. Information about the geographical coordinates of these traps will enable us to apply the kriging spatial analysis tool to generate maps with the predicted numbers of Ae. aegypti. Satellite images were used to identify the characteristic features the four areas, so that other areas could also be classified using only the sensing remote images. The developed methodology enables the identification of high-risk areas for Ae. aegypti and for the occurrence of Dengue, as well as Zika fever and Chikungunya fever using only sensing remote images. These results allow health and vector control services to prioritize these areas for developing surveillance and control measures. The use of the available resources can be optimized and potentially promote a decrease in the expected incidences of these diseases, particularly Dengue. [ABSTRACT FROM AUTHOR]

Accurate land subsidence prediction is fundamental for effective management of land subsidence and other associated geohazards. Lagos, the most densely populated city in Nigeria, has been adversely affected by widespread land subsidence. Previous studies have largely been restricted to analyzing space geodetic technique data. Unfortunately, this technique imposes spatial limitations resulting in incomplete coverage of the study areas due to atmospheric effects, orbital satellite characteristics, or surface characteristics. The application of geostatistics to georeferenced data obtained from space-based measurements can reliably provide complete information on spatial variability. The objective of this study is to provide an overview of current subsidence rates and to assess the spatial variability of land subsidence in the study area by using geostatistics that integrate semivariogram and ordinary kriging. The study area was partitioned into eight subregions; for each subregion, the experimental variogram was determined from the observed data and fitted to the optimal models. Seven subregions were fitted to exponential models and one was fitted to the spherical model. The model semivariograms were used in the kriging analysis to estimate the spatial variability of subsidence rates. The results showed that the nugget-to-sill ratio lies between 44 and 70%, indicating that the spatial variation of subsidence rates at the scale under study is moderately distributed and the mechanism of deformation is similar. The nugget effect of the subsidence rate is between 0.25 and 0.75, indicating that subsidence rates are influenced by various contributions. The predicted spatial variability of subsidence rates by Ordinary Kriging presents reliable values of R-squared (0.32–0.42) and RMSE (0.21–0.30). Critical subsidence rates characterised the areas around the Atlantic Ocean coastal alluvium deposit, the Lagos lagoon, and the Ogun River flood alluvium. This study successfully demonstrates the suitability of the geostatistical tools to evaluate spatial variability of subsidence rates.

Background. Several studies have demonstrated that chromium (Cr) and cadmium (Cd) have adverse impacts on the environment and human health. These elements are present in electronic waste (e-waste) recycling sites. Several interpolation methods have been used to evaluate geographical impacts on humans and the environment. Objectives. The aim of the present paper is to compare the accuracy of inverse distance weighting (IDW) and ordinary kriging (OK) in topsoil analysis of e-waste recycling sites in Douala, Cameroon. Methods. Selecting the proper spatial interpolation method is crucial for carrying out surface analysis. Ordinary kriging and IDW are interpolation methods used for spatial analysis and surface mapping. Two sets of samples were used and compared. The performances of interpolation methods were evaluated and compared using cross-validation. Results. The results showed that the OK method performed better than IDW prediction for the spatial distribution of Cr, but the two interpolation methods had the same result for Cd (in the first set of samples). Results from Kolmogorov-Smirnov and Shapiro-Wilk tests showed that the data were normally distributed in the study area. The p value (0.302 and 0.773) was greater than 0.05 for Cr and for Cd (0.267 and 0.712). In the second set of samples, the OK method results (for Cd and Cr) were greatly diminished and the concentrations dropped, looking more like an average on the maps. However, the IDW interpolation gave a better representation of the concentration of Cd and Cr on the maps of the study area. For the second set of samples, OK and IDW for Cd and Cr had more similar results, especially in terms of root mean square error (RMSE). Conclusions. Many parameters were better identified from the RMSE statistic obtained from cross-validation after exhaustive testing. Inverse distance weighting appeared more adequate in limited urban areas. Competing Interests. The authors declare no competing financial interests

Based on Open CASCADE and Ordinary Kriging interpolation algorithm, a software development framework adding geostatistical interpolation algorithm to CAD geometry operation core for three-dimensional hydrogeological modeling is designed. Using three-dimensional graphics rendering of Open CASCADE, visual interaction, editing functions and geo-statistics interpolation function of Ordinary Kriging, we designed the hydrogeology modeling software for mine by taking Visual Studio as development tool, C++ and Python as development language, and SQLite as geological database, developed and Hydrogeo3D mine hydrogeology modeling software to realize the editable function of local details in the modeling process.

In our article the ordinary kriging interpolation method was used for a spatial presentation of PM2.5 concentrations. The data used in the research was obtained from the unique PM2.5 measuring system, based on low-cost optical sensors for PM2.5 concentration measurements, working on Wroclaw University of Science and Technology campus area. The data from this system was used as an input for the interpolations that were made for three different days characterized by the highest measured values of PM2.5 – 20.01.2019, 17.02.2019 and 30.03.2019. For each of the selected days, variants with the maximum and minimum PM2.5 values recorded on a given measurement day were presented. In the analyses performed, the ordinary kriging technique and cross-validation, was used as the interpolation and the validation method, respectively. Parameters determining the quality of performed interpolation were Mean Error, Mean Standardized Error, Root Mean Square Error, and Average Standard Error. As the main indicator of quality of interpolation RMSE parameter was used. Analysis of that parameter shows that the higher variability of the data used for interpolation affects its quality. The Root Mean Square Error parameter reached 0.64, 0.94 and 1.71 for the lowest concentrations variants characterized by low spatial variability, and 6.53, 7.51, 11.28 for the highest one, which were characterized by high spatial variability. The obtained results of the research with the use of GIS tools shows that the ordinary kriging method allowed for the correct spatial presentation of the PM2.5 concentration variability in areas not covered by the measurement system.

The present investigation in the Tiruvannamalai region is about high fluoride contamination of groundwater samples from bore wells and open wells. About 75% of groundwater samples were found predominantly containing the fluoride content greater than the acceptable limit of 1.5 mg/L in the ranges 1.51 – 2.00 mg/L (23%), 2.01 – 3.00 mg/L (36%) and greater than or equal to 3.01 mg/L (16%) as per WHO. The other water quality parameters were found within the permissible limit of WHO. Taking the groundwater sources into consideration, the non – carcinogenic risk due to high fluoride concentration in groundwater sources revealed that teen – aged (98%), Children (92%) and Infant (98%) categories were at greater risk than those under Men (50%) and Women (69%) categories. The mapping was done on the spatial distribution of fluoride concentration in groundwater and the associated health risk by Ordinary Kriging. The correlation coefficients among the parameters witnessed that the hydro-chemical facies are interdependent. Box – Whisker plots illustrated the dispersion of various water quality parameters. The WQI data represented the quality of groundwater in view of potable nature due to dissolved ions. The Gibbs, bivariate mixing and the scatter plots ascribed the dissolution of carbonate and silicate minerals which dominate the groundwater chemistry. The factor analysis detailed the extracted loadings of different parameters of groundwater sources and differentiated the percentage variance values between bore well and open well sources.