Geostatistical Mapping Research Articles

Geostatistical Mapping of Groundwater Salinity and Seawater Intrusion in Coastal Aquifers of Jember Regency Using Physicochemical Parameters and Seawater Fraction

Geostatistical Mapping of Groundwater Salinity and Seawater Intrusion in Coastal Aquifers of Jember Regency Using Physicochemical Parameters and Seawater Fraction

A decision-supporting system for vineyard management: a multi-temporal approach with remote and proximal sensing

PurposeSite-specific field management operations represent one of the fundamental principles of precision viticulture. The purpose of the research is to observe and analyse the evolution of a vineyard over three consecutive years to understand which factors most significantly influence the quality of the vineyard’s production.MethodsThe research involved technologically advanced tools for crop monitoring, such as remote and proximal sensors for vegetation surveys. In association, grape quality analyses were performed through laboratory analysis, constructing geostatistical interpolation maps and matrix correlation tables.ResultsBoth remote and proximal sensing instruments demonstrated their ability to effectively estimate the spatial distribution of vegetative and quality characteristics within the vineyard. Information obtained from GNDVI and CHM proved to be valuable and high-performance tools for assessing field variability. The differentiated plant management resulted in uniform production quality characteristics, a change evident through the monitoring techniques.ConclusionThe research highlights the effectiveness of using advanced technological instruments for crop monitoring and their importance in achieving uniformity in production quality characteristics through differentiated plant management. From the results obtained, it was possible to observe how differentiated plant management led to a uniformity of production quality characteristics and how the monitoring techniques can observe their evolution. This result represents a positive accomplishment in field management during the three monitoring years, responding to the principles and objectives of precision agriculture.

Assessing landslide susceptibility in northern Morocco: A geostatistical mapping approach in Al Hoceima-Ajdir

Landslides are a significant global risk due to their impact on nature, society, and the economy. The Al Hoceima-Ajdir region is highly susceptible to geohazards such as earthquakes and ground instabilities caused by local tectonic movements and specific climatic and geomorphological conditions. This study aims to assess landslide susceptibility in Al Hoceima-Ajdir using a digital mapping approach that integrates the geostatistical Frequency Ratio tool, geomatic tools, and field observations. The frequency ratio method, a widely used technique in geospatial analysis, evaluates the relative frequency of landslides based on the spatial distribution of conditioning factors such as slope, lithology and land use – land cover. This method calculates the ratio of the frequency of landslides within specific classes of these factors to their respective frequencies in the entire study area. This research introduces a novel susceptibility map highlighting major threats to several natural and touristic sites crucial to the region's socio-economic activities. The susceptibility map was developed by analyzing 40 historical landslides along various predisposing factors, including topographic, geo-hydrological, and anthropogenic parameters. The results identify areas prone to landslides and classify their susceptibility into five levels ranging from very high to very low, with each level indicating a different degree of risk and requiring a specific set of preventive measures. The reliability of this map was evaluated using the Receiver Operating Characteristics (ROC) method, demonstrating a high accuracy of 89%, further validated by field visits. This high-precision susceptibility map is significant for pre- and post-risk management, enhancing prevention and mitigation strategies. It also protects society, the economy, and the environment by providing a reliable regional planning and hazard management tool. The findings of this study offer a significant advancement in the methodology for landslide susceptibility assessment and present a model that can be adapted to other regions facing similar geohazards. The research underscores the importance of integrating geostatistical tools with field data to produce accurate and actionable susceptibility maps, benefitting the global scientific community by improving hazard prediction and management strategies.

Geostatistical Mapping and Assessment of Iron Concentrations in Groundwater

Geostatistical Mapping and Assessment of Iron Concentrations in Groundwater

Revealing soil microbial ecophysiological indicators in acidic environments laden with heavy metals via predictive modeling: Understanding the impacts of black diamond excavation

Appraising the activity of soil microbial community in relation to soil acidity and heavy metal (HM) content can help evaluate it's quality and health. Coal mining has been reported to mobilize locked HM in soil and induce acid mine drainage. In this study, agricultural soils around coal mining areas were studied and compared to baseline soils in order to comprehend the former's effect in downgrading soil quality. Acidity as well as HM fractions were significantly higher in the two contaminated zones as compared to baseline soils (p < 0.01). Moreover, self-organizing and geostatistical maps show a similar pattern of localization in metal availability and soil acidity thereby indicating a causal relationship. Sobol sensitivity, cluster, and principal component analyses were employed to enunciate the relationship between the various metal and acidity fractions with that of soil microbial properties. The results indicate a significant negative impact of metal bioavailability, and acidity on soil microbial activity. Lastly, Taylor diagrams were employed to predict soil microbial quality and health based on soil physicochemical inputs. The efficiency of several machine learning algorithms was tested to identify Random Forrest as the best model for prediction. Thus, the study imparts knowledge about soil pollution parameters, and acidity status thereby projecting soil quality which can be a pioneer in sustainable agricultural practices.

Elucidating the synergistic effect of acidity and metalloid poisoning on the microbiome through metagenomics and machine learning approaches

The abundance and diversity of the microflora in a complex environment such as soil is everchanging. Mica mining has led to metalloid poisoning and changes in soil biogeochemistry affecting the overall produce and leading to toxic dietary exposure. The study focuses on two prominent stressors acidity and arsenic, in mining-contaminated agricultural locations. Soil samples were collected from agricultural fields at a distance of 50 m (zone 1) and 500 m (zone 2) from active mines. Mean arsenic concentration was higher in zone 1 and pH was lower. Geostatistical and self-organizing maps were employed to report that the pattern of localization of soil acidity and arsenic content is similar indicating a causal relationship. Cluster and principal component analysis were further used to materialize a negative effect of soil acidity fractions and arsenic labile pool on soil enzymatic activity (fluorescein diacetate, dehydrogenase, β-1,4-glucosidase, phosphatase, and urease), respiration and Microbial biomass carbon. Soil metagenomic analysis revealed significant differences in the abundance of microbial populations with zone 1 (contaminated zone) having lower alpha and beta diversity. Finally, the efficacy of several machine-learning tools was tested using Taylor diagrams and an effort was made to select a potent algorithm to predict the causal stressors responsible for depreciating soil microbial health. Random Forrest had superior predictive power based on numerical evidence and was therefore chosen as the best-fitted model. The aforementioned insights into soil microbial health and sustenance in stressed conditions can be beneficial for predicting remedial strategies and practicing sustainable agriculture.

Exploring the uncertainty of machine learning models and geostatistical mapping of rare earth element potential in Indiana coals, USA

Rare earth elements and yttrium (REEs) have a wide range of applications in high- and low-carbon technologies. The strategic significance of REEs has grown due to their expanding applications in manufacturing industries and the constrained availability of these essential resources. This research explores the applicability of machine learning models and their uncertainty for assessing the REE potential in coal beds using various coal parameters as inputs. The work focuses on developing a predictive model based on geological variables, excluding considerations related to potential shifts in the commodities market. The Indiana Coal Quality Database was used as the data source. The promising and unpromising indicators derived from the outlook coefficient of samples from the database were used as the REE potential indicator for machine learning classification models. The filter-based approach with bootstrap was used to evaluate the importance of the coal parameters and their prediction uncertainties. Four machine learning methods (linear discriminant analysis (LDA), random forest (RF), support vector machine (SVM), and artificial neural networks (ANN), a data balancing and augmentation approach (Synthetic Minority Over-sampling Technique), and bootstrap resampling techniques were used for building the models and evaluating their prediction capabilities under uncertainty. It was determined that the SVM bootstrap model with ten-times balanced and augmented data provided superior results compared with other models. Finally, stochastic spatial maps of the REE potential within the coal basin were generated using sequential indicator simulation. The spatial maps of the REE potential showed that a 29% area of the Indiana section of the Illinois coal basin has economic potential of REEs, with 90% confidence.

Management Zones Delineation, Correct and Incorrect Application Analysis in a Coriander Field Using Precision Agriculture, Soil Chemical, Granular and Hydraulic Analyses, Fuzzy k-Means Zoning, Factor Analysis and Geostatistics

The objective of our investigation was to study the various effects of correct and incorrect application of fuzziness exponent, initial parameterization and fuzzy classification algorithms modeling on homogeneous management zones (MZs) delineation of a Coriandrum sativum L. field by using precision agriculture, soil chemical, granular and hydraulic analyses, fuzzy k-means zoning algorithms with statistical measures like the introduced Percentage of Management Zones Spatial Agreement (PoMZSA) (%), factor and principal components analysis (PCA) and geostatistical nutrients GIS mapping. Results of the exploratory fuzzy analysis showed how different fuzziness exponents applied to different soil parameter groups can reveal better insights for determining whether a fuzzy classification is a correct or incorrect application for delineating fuzzy MZs. In all cases, the best results were achieved by using the optimal fuzziness exponent with the full number of parameters of each soil chemical, granular and hydraulic parameter group or the maximum extracted PCAs. In each case study where the factor analysis and PCA showed optimal MZs &gt; 2, the results of the fuzzy PoMZSA clustering metric revealed low, medium and medium to high spatial agreement, which presented a statistically significant difference between the soil parameter datasets when an arbitrary or commonly used fuzziness exponent was used (e.g., φ = 1.30 or φ = 1.50). Soil sampling and laboratory analysis are tools of major significance for performing exploratory fuzzy analysis, and in addition, the FkM Xie and Benny’s index and the introduced fuzzy PoMZSA clustering metric are valuable tools for correctly delineating management zones.

Assessing and geostatistical mapping of metal contamination in the polar arable plot (Yamal-Nenets Autonomous District, Russia)

Assessing and geostatistical mapping of metal contamination in the polar arable plot (Yamal-Nenets Autonomous District, Russia)

Spatial variation of pure and simple shear across transpressive shear zones: flow kinematics map of the Posada-Asinara shear zone (NE Sardinia, Italy)

Flow kinematics estimations are necessary to determine how the deformation is partitioned in shear zones at different scales. Combining different scales of structural analysis with quantitative vorticity constraint allows a more rigorous examination of the kinematics of deformation. The Posada-Asinara shear zone (PASZ) in northern Sardinia (Italy) is a crustal-scale km-thick transpressive shear zone, separating the High- from the Medium-Grade Metamorphic Complex. After microstructural analysis, the vorticity of the flow was estimated to investigate the southernmost boundary of the PASZ, highlighting a dominant pure shear component far from the core of the shear zone. These results have been integrated with existing data to derive a regional-scale flow kinematic map of the Baronie region, highlighting the potentiality of automated geostatistical mapping to become a powerful complementary tool in the investigation of flow kinematics in collisional environments.

Spatial distribution of hg and as in rivers of the eastern amazon impacted by gold mining

Human activities contribute to the contamination of rivers by toxic elements, causing serious effects on the health of traditional communities in the Amazon region. With the aim of verifying the spatial distribution of Hg and As concentrations in a region affected by gold mining in the Baú, Curuá, and Curuaés River basin, in the municipality of Altamira-PA, in the Brazilian Amazon, a study was carried out using geostatistical maps. The quantification of elements was performed using Inductively Coupled Plasma Emission Spectrometry with hydride generation (HGICP-OES). The obtained values were exported for use in computational processes with the QGIS and Surfer software, in order to produce isotheor maps. The results of the maps showed Hg concentrations that exceeded the limit established by the National Council for the Environment, which is 0.2 µg L-1. This indicates that the presence of mining in the region represents a threat to the ecosystem of these rivers. On the other hand, the maps for As showed that no station presented results that exceeded concentrations above the limit allowed for this element, which is 10 µg L-1. Based on these results, the geostatistical maps were effective in monitoring areas affected by toxic elements and can be used as a tool in the control of river water quality.

Spatial distribution and risk associated with shot hole disease in apricot (Prunus armeniaca L.) in Northern Pakistan

Wilsonomyces carpophilus is the major fungus that produces shot hole disease (SHD) in apricots, which results in significant economic losses. This problem can be addressed through the monitoring of the diseases and spotlighting the risks connected with them. This study employed descriptive and geostatistical mapping techniques to track the spatial distribution and risk of SHD in apricot orchards in five apricot-growing valleys. The results showed a spatial distribution of the disease within the study area even varying within the valleys and orchards. A moderate spatial dependence of SHD prevalence (0.524), incidence (0.363), and severity index (0.313) were observed. GIS maps and trend analysis graphs also showed the spatial distribution of the disease from east-west and south-north directions. Climate and cultural practices favor the perpetuation of pathogens and the spread of SHD. This information will help apricot growers regarding disease management and improve sustainable profitability.

Spatial Analysis of Soil Texture using GIS based Geostatistics Models and Influence of Soil Texture on Soil Hydraulic Conductivity in Melur Block of Madurai District, Tamil Nadu

Background: Soil texture is one of the important property constitute ultimate size particles. Proportions of these ultimate soil particles depend on the pedogenesis and mineralogical composition of parent material and also decide all other soil properties. Due to the imprudent crop selection and imperfect agricultural methods, the very fertile soil is becoming denser and low productive. Geostatistics is a set of Models and tools developed for statistical analysis of continuous data. A critical aspect of agriculture ecosystem and environment modeling is the accurate prediction for the spatial variability of soil properties. Methods: In this study, Geostatistical modeling based Kriging techniques has been used to identify the textural variation of soil in the present scenario of Melur Block of Madurai district. Totally, 150 soil samples were collected across Melur block of Madurai district based on grid soil survey and geo coordinates. Result: Geostatistical soil textural maps have been generated based on Kriging tools and these would be utilized for Land Use Pattern, precision agriculture, conservation practices, as well as land management in the research region. The characterization of the spatial variability and influence of soil texture on hydraulic conductivity is essential to accomplish a better understanding of intricate relations between soil properties and environmental factors.

Risk Assessment and Spatial Distribution of Heavy Metals with an Emphasis on Antimony (Sb) in Urban Soil in Bojnourd, Iran

Recently, one of the major environmental issues is the pollution of soil with Antimony (Sb), which is ecologically detrimental and potentially carcinogenic to humans. In developing countries such as Iran, Sb concentrations in soils have not yet been accurately determined. Therefore, the purpose of this study was to examine the levels of Sb and the other HMs (Pb, Cd, As, Ni, Hg and Cr) in the surface soils of Bojnourd, Iran, as well as their distribution and potential risks to the environment and human health. A total of 37 soil samples (0–20 cm) were taken from different sampling stations: 900 × 900 m regular grid for traffic areas (TA), residential areas (RA) and suburb areas (SA). The contamination factor (CF) and geo-accumulation index (Igeo) are two indices that were used to reflect the potential ecological risk from HMs. Geographic information system (GIS), Spearman correlation matrix and health risk indexes were utilized to investigate the source and potential hazard of HMs. The results showed that the average concentration of HMs in TA was highly enriched compared to other areas. Most soil samples were identified to have low levels of Sb and Ni pollution, while having moderate to high levels of Pb, Cr, As, Hg and Cd pollution, as determined by the pollution indices (Igeo and CF). Geostatistical analysis and GIS mapping of the spatial distribution of HM concentrations showed that there have been similar patterns of spatial distribution for Cd, Cr, Ni and Sb and their hot spots were in the southeast, west and center of the city. Neither the hazard quotient (HQ) nor the hazard index (HI) of the examined HMs indicated any non-carcinogenic risk to adults or children. However, carcinogenic risk assessment revealed that cancer risk was raised from Cr and Cd contents for children, while these elements showed an acceptable risk for adults. Furthermore, children’s carcinogenic and non-carcinogenic values were greater than adults’, indicating more potential health hazards associated with these HMs. Therefore, assessing the risk posed by HM pollution in urban surface soil is vital and urgent for children. A more detailed investigation is also required to identify the spatial distribution of soil pollution in areas recognized as enriched in Sb. A proper assessment of the environmental risk and the corresponding risk to humans from HM in a study area can be critical to developing an appropriate remediation method.

Geostatistical Analysis of Apricot Shot Hole Disease and Influence Factors in District Nagar, Gilgit-Baltistan, Pakistan

Apricot (Prunus armeniaca L.) is the most common, high-income generative fruit and consequently plays an important part in the region's socio-economic growth. However, apricots are susceptible to several diseases and pests that cause lower yields and significant financial losses, including apricot shot hole disease brought on by Wilsonomyces carpophilus, a polyphagous fungus, creates a serious threat to stone fruits, particularly the apricot tree. The current study used descriptive statistics and geostatistical approaches to evaluate the geographical distribution of the apricot shot hole disease (5 valleys, 30 orchards, and 480 apricot trees) in district Nagar, Gilgit-Baltistan, Pakistan. From each apricot tree, 150 apricot fruits, a total of 72,000, were randomly selected to assess the incidence, disease index, and severity. Results revealed that the disease is spatially distributed within the study area, varying within valleys and orchards. The range of disease prevalence was 50.0-67.70%, incidence 56.97-64.01%, index 32.49-40.56% and mean severity 0.21-0.24%. Geostatistical techniques were used to predict the spatial dependency class. Results revealed that disease prevalence had a nugget/sill ratio of 0.915, inferring weak spatial dependence, whereas incidence, disease index, and severity inferred moderate spatial dependence with nugget/sill ratios of 0.479, 0.628, and 0.393, respectively. Moreover, the spherical semivariogram model, trend analysis graph, and GIS maps indicate the region's spatial distribution of apricot shot hole disease. This information linked to metrological data (temperature, precipitation, and relative humidity) seemingly favored the apricot shot hole disease development during the growing period. Evaluation of geostatistical mapping strategies in monitoring spatial distributions of apricot shot hole disease in a field setting will improve the decision support for disease management, selection of resistant variety, and improve sanitation condition of apricot orchards. Continuous climate and pest monitoring are essential for creating an efficient disease warning system for farmers and other organizations involved in agriculture to prevent future plant epidemics.

GeoSPM: Geostatistical parametric mapping for medicine

The characteristics and determinants of health and disease are often organized in space, reflecting our spatially extended nature. Understanding the influence of such factors requires models capable of capturing spatial relations. Drawing on statistical parametric mapping, a framework for topological inference well established in the realm of neuroimaging, we propose and validate an approach to the spatial analysis of diverse clinical data-GeoSPM-based on differential geometry and random field theory. We evaluate GeoSPM across an extensive array of synthetic simulations encompassing diverse spatial relationships, sampling, and corruption by noise, and demonstrate its application on large-scale data from UK Biobank. GeoSPM is readily interpretable, can be implemented with ease by non-specialists, enables flexible modeling of complex spatial relations, exhibits robustness to noise and under-sampling, offers principled criteria of statistical significance, and is through computational efficiency readily scalable to large datasets. We provide a complete, open-source software implementation.

Polychlorinated dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs) soil contamination in Lausanne, Switzerland: Combining pollution mapping and human exposure assessment for targeted risk management

In December 2020, high soil concentrations of polychlorinated dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs) were discovered across large parts of Lausanne, Switzerland. Concentrations reached up to 640 ng TEQWHO-2005/kg dry weight. The most likely source was a former municipal waste incinerator. A three-step, multidisciplinary approach to human health risk assessment was conducted to determine the potential population exposure to PCDD/Fs and identify appropriate preventive measures. First, exposure scenarios were developed based on contaminated land uses. Second, the toxicological risks of different scenarios were evaluated using a toxicokinetic model estimating increases in blood serum PCDD/F concentrations over background concentrations from the general population's food consumption. Third, a detailed geostatistical mapping of PCDD/F soil contamination was performed. Stochastic simulations with an external drift and an anisotropic model of the variogram were generated to incorporate the effects of distance from emission source, topography, and main wind directions on the spatial distribution of PCDD/Fs in topsoil. Three main scenarios were assessed: i) direct ingestion of soil by children in playgrounds; ii) consumption of vegetables from private gardens by children and adults; and iii) consumption of food from livestock and poultry raised on contaminated soil. The worst exposure scenario involved the consumption of eggs from private hen houses, resulting in PCDD/F concentrations in serum an order of magnitude higher than might normally be expected. No relevant increases in serum concentrations were calculated for direct soil ingestion and vegetable consumption, except for cucurbitaceous vegetables. Combining mapping and exposure scenario assessment resulted in targeted protective measures for land users, especially concerning food consumption. The results also raised concerns about the potential unsafe consumption of products derived from animals raised on land with PCDD/F concentrations only moderately over environmental background levels.

Mapping Soil and Pasture Attributes for Buffalo Management through Remote Sensing and Geostatistics in Amazon Biome.

Simple SummaryBuffalo breeding in the Amazon biome can contribute significantly to local community development and, thus, is considered an essential income source. However, in Amazon regions, the inadequate breeding of these animals can lead to considerable negative consequences for the environment. Therefore, it is crucial to develop methodologies to improve animal management and grass yield. One of these methodologies is related to Precision Agriculture (PA), adapted for pasture and animal monitoring. Along these lines, we seek to utilize geostatistical techniques and remote sensing applications to better understand Buffalo grazing under a rotating system. In particular, we analyze forage Dry and Green Matter, as well as pH in pasture soils, demonstrating the obstacles against and advantages of the implementation of precise techniques for decision making and increasing grass productivity. We describe ways in which geostatistical soil pH mapping can be conducted, as well as the premises necessary to include remote sensing data in the analysis of pasture variables. Implementing these results in buffalo management systems can contribute to greater productivity and increasingly sustainable livestock.The mapping of pastures can serve to increase productivity and reduce deforestation, especially in Amazon Biome regions. Therefore, in this study, we aimed to explore precision agriculture technologies for assessing the spatial variations of soil pH and biomass indicators (i.e., Dry Matter, DM; and Green Matter, GM). An experiment was conducted in an area cultivated with Panicum maximum (Jacq.) cv. Mombaça in a rotational grazing system for dairy buffaloes in the eastern Amazon. Biomass and soil samples were collected in a 10 m × 10 m grid, with a total of 196 georeferenced points. The data were analyzed by semivariogram and then mapped by Kriging interpolation. In addition, a variability analysis was performed, applying both the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) derived from satellite remote sensing data. The Kriging mapping between DM and pH at 0.30 m depth demonstrated the best correlation. The vegetative index mapping showed that the NDVI presented a better performance in pastures with DM production above 5.42 ton/ha−1. In contrast, DM and GM showed low correlations with the NDWI. The possibility of applying a variable rate within the paddocks was evidenced through geostatistical mapping of soil pH. With this study, we contribute to understanding the necessary premises for utilizing remote sensing data for pasture variable analysis.

Large-scale geostatistical mapping and occupancy-abundance patterns of Cerambyx species threatening SW Spain oak forests

Large-scale geostatistical mapping and occupancy-abundance patterns of Cerambyx species threatening SW Spain oak forests

Geostatistical Mapping of Folded Itabiritic Rocks of the Bonito Mine, Northeastern Brazil

Geological modeling is the primary task of any exploratory geological investigation, and it is performed even during the mine development. In the study area, the Serra dos Quintos Formation hosts banded iron formations represented by assorted itabirites. The database used in this work was exploited from a geological databank which was structured to gather data and information depicted from an exhaustive exploratory drilling program. A survey was performed throughout the entire databank to collect the available thickness data values (measured in meters) from drill core logs that intersected the itabirite. Since structural heterogeneities can occur within these rocks, this study aims to identify such features. Geostatistical estimation and simulation methods were employed to map folded itabiritic beds based on thickness data accurately. Kriging estimators are often used for practical reasons; however, sometimes, the estimates can be smoothed and do not represent the entire original data range. Simulation algorithms can yield several stochastic images, but local accuracy cannot always be guaranteed. Simulated annealing was performed by adjusting the global statistics and preserving the local accuracy. We demonstrated that the banded iron formations’ thicker areas might correspond to the antiform fold as the dominant tectonic feature in the study area. Finally, we show that the simulated thickness map discloses the thicker mineralized spots. Meanwhile, the thinner ones may unveil intrinsic structural heterogeneities mainly observed at the limbs of the Bonito fold, where intensive deformation within the itabiritic layer was higher than expected. Concerning the mining issues, information obtained from the simulated thickness map could provide ancillary data to improve mining planning in the study area.