Search Results (359)

This study assessed whether stratifying agricultural areas into macro- and micro-variability regions allows targeted sampling to better capture soil attribute variability, thus improving digital soil maps compared to regular grid sampling. Allocating more samples where soil variability is expected offers a promising alternative. We evaluated two sampling densities in two agricultural fields in Southeast Brazil: a sparse density (one sample per 2.5 hectares), typical in Precision Agriculture, and a denser grid (one sample per hectare), which usually provides reasonable mapping accuracy. For each density, we applied three designs: a regular grid and grids with 25% and 50% guided points. Apparent soil magnetic susceptibility (MSa) delimited macro-homogeneity zones, while Sentinel-2’s Enhanced Vegetation Index (EVI) identified micro-homogeneity, guiding sampling to pixels with higher Fuzzy membership. The attributes assessed included phosphorus (P), potassium (K), and clay content. Results showed that the 50% guided sample configuration improved ordinary kriging interpolation accuracy, particularly with sparse grids. In the six sparse grid scenarios, in four of them, the grid with 50% of the points in regular design and the other 50% directed by the proposed method presented better performance than the full regular grid; the higher improvement was obtained for clay content (RMSE of 54.93 g kg⁻¹ to 45.63 g kg⁻¹, a 16.93% improvement). However, prior knowledge of soil attributes and covariates is needed for this approach. We therefore recommend two-stage sampling to understand soil properties’ relationships with covariates before applying the proposed method. Full article

From a global ecological management perspective, as a core tree species in the mountain ecosystem of Xinjiang, the study of the spatial distribution characteristics of Picea schrenkiana var. tianschanica is crucial for maintaining the ecological balance in the Tianshan region. This study focuses on the western section of the Tianshan mountains in Xinjiang and employs the variogram analysis technique to explore the spatial heterogeneity of Picea schrenkiana var. tianschanica biomass. Successively, the study implements ordinary kriging, multivariate linear regression, the random forest algorithm, and an innovative random forest residual kriging method to conduct a spatial interpolation analysis of Picea schrenkiana var. tianschanica biomass in the target area. The results indicate that the biomass of Picea schrenkiana var. tianschanica exhibits moderate spatial autocorrelation, with its distribution pattern being influenced by a combination of topography, climate, and soil conditions. After comparing multiple spatial interpolation methods, it is found that the hybrid model combining regression analysis and kriging, delivers the best performance (R² = 0.642, RMSE = 40.18, RMSPE = 44.6). This model not only significantly improves the prediction accuracy, but also provides an intuitive and accurate spatial distribution map of Picea schrenkiana var. tianschanica biomass in the western section of the Tianshan mountains which reveals the global ecological importance of Picea schrenkiana var. tianschanica in an intuitive and accurate way, providing valuable scientific evidence and practical guidance for the field of international ecological protection and resource management. Full article

Accurate soil total phosphorus (TP) prediction is essential to support sustainable agricultural practices and formulate ecological conservation protection policies, particularly in complex karst landscapes with high spatial variability and high phosphorus and cadmium content and interactions, complicating nutrient management. This study uses GIS and geostatistical methods to analyze the spatial distribution, influencing factors, and predictive modeling of soil TP in the karst region of northern Mashan County, Guangxi, China. Using 427 surface soil samples, we developed five predictive models: ordinary kriging (OK), regression kriging (RK) and geographically weighted regression kriging (GWRK) combined with environmental variables such as land uses, soil types, and topographic factors; residual mean-centered kriging (MM_OK), and residual median-centered kriging (MC_OK). Our results indicate that higher TP levels were observed in agricultural lands (paddy fields and dry land, at 766 and 913 mg·kg⁻¹, respectively) may due to fertilization, while forests and shrublands showed lower TP levels (383 and 686 mg·kg⁻¹, respectively), reflecting natural phosphorus cycling. The high-value areas of soil TP concentration are in the karst areas in the west and east of the study area, and the low-value area is in the Hongshui River valley in the north of Mashan. The spatial distribution of soil TP is affected by land use, soil type, and topography. The GWRK model exhibited superior accuracy (80.6%), with predicted concentration of TP closely aligning with observed TP values, effectively capturing fine spatial variations, and showing the lowest mean standardized error, average standard error, and mean absolute error. GWRK also achieved the highest R² (0.67), demonstrating robust predictive capability. MM_OK and MC_OK models performed well and showed smoother spatial transitions, while the OK model displayed the lowest predictive accuracy (62%). By utilizing spatially adaptive weighting, GWRK and its residual-centered kriging method improve soil TP’s prediction accuracy and smoothness in karst areas, providing a reference for targeted soil conservation and sustainable agricultural practices in spatially complex karst environments. Full article

Soil acidity is a major limiting factor for potato production in Peru’s high Andean region. This study aims to predict the spatial variability of soil acidity as a fundamental tool for recommending site-specific liming treatments and to identify the physical–chemical characteristics most closely related to soil acidity. The soil samples were collected from five locations in the province of Pachitea, Huánuco. Descriptive statistics, principal component analysis (PCA), and Pearson correlation analysis were used to identify the soil properties contributing most to total variance and those most strongly correlated with soil acidity. The ordinary geostatistical kriging method evaluated the predictive accuracy for 23 soil properties and liming requirements over a 28,463 ha area, at a spatial resolution of 10 m. Results showed that the Plaza Punta and Buenos Aires locations had more degraded acidic soils, with frequencies between 55% and 100% above the general mean (30.94 ± 24.87%) and the critical threshold (25%) for potato cultivation. Variables such as exchangeable calcium percentage (ECP), Ca²⁺, Mg²⁺, sand content, and organic matter strongly correlated with soil acidity, while exchangeable H⁺ and ECP were the main contributors to the total variance. Geostatistical analysis revealed that Mg²⁺ and Ca²⁺ had the highest R² values (0.87 and 0.76, respectively), indicating a strong fit between observed and predicted values in the spatial analysis of soil acidity. It is concluded that the agricultural dolomite requirements in the localities of Plaza Punta and Buenos Aires exhibit high spatial predictability. Additionally, the analysis of diverse soil physicochemical properties is emphasized as critical for determining precise application rates. Full article

The spectral reflectance measured in situ is often regarded as the “truth”. However, its limited coverage and large spatial heterogeneity often make the ground-based reflectance unable to represent the remote sensing images. Since the spatial scale mismatch between ground-based, airborne, and spaceborne measurements, the applications of geological exploration, metallogenic prognosis and mine monitoring are facing severe challenges. In order to explore the influence of spatial scale effect on rock spectra, spectral reflectance with uncertainty caused by differences in illumination view geometry and spatial heterogeneity is introduced into the Bayesian Maximum Entropy (BME) method. Then, the rock spectra are upscaled from the point-scale to meter-scale and to 10 m-scale, respectively. Finally, the influence of spatial scale effect is evaluated based on the reflectance value, spectral shape, and spectral characteristic parameters. The results indicate that the BME model shows better upscaling accuracy and stability than Ordinary Kriging and Ordinary Least Squares model. The maximum Euclidean Distance of rock spectra caused by spatial resolution change is 6.271, and the Spectral Angle Mapper can reach 0.370. The spectral absorption position, absorption depth, and spectral absorption index are less affected by scale effect. For the area with similar spatial heterogeneity to the Huangshan Copper–Nickel Ore District, when the spatial resolution of the image is greater than 10 m, the rock’s spectrum is less influenced by the change in spatial resolution. Otherwise, the influence of spatial scale effect should be considered in applications. In addition, this work puts forward a set of processes to evaluate the influence of spatial scale effect in the study area and carry out the upscaling. Full article

Data interpolation methods are important statistical analysis tools that can fill in data gaps and missing areas by predicting and estimating unknown data points, thereby improving the accuracy and credibility of data analysis and research. Different interpolation methods are widely used in related fields, but the error between different interpolation methods and their interpolation fusion optimization have a significant impact on the interpolation accuracy, which still deserves further exploration. This study is based on two different types of point data: PM_2.5 (PM_2.5 refers to particulate matter in the atmosphere with a diameter of 2.5 μm or less, also known as inhalable particles or fine particulate matter) in Xinyang City, Henan Province, and the elevation of typical gullies in Yuanmou County, Yunnan Province. Using relative difference coefficients and hotspot analysis methods, the differences in error characteristics among four interpolation methods, ordinary kriging (OK), universal kriging (UK), inverse distance weighted (IDW), and radial basis functions (RBFs), were compared, and the influence of interpolation fusion methods on the accuracy of interpolation results was explored. The results show that after interpolation of PM_2.5 concentration and gully elevation, the error difference between OK and UK is the smallest in both datasets. For PM_2.5 concentration data, IDW and UK interpolation errors have the largest difference; for elevation data, the differences between RBF and UK interpolation are the largest. The weighted fusion results show that the interpolation error accuracy of PM_2.5 concentration data with an interpolation point density of 0.009 points per square kilometer is improved, and the root mean square error (RMSE) after fusion is reduced from 0.374 μg/m³ to 0.004 μg/m³. However, the error accuracy of the elevation data of the gully with an interpolation point density of 0.76 points/m² did not improve significantly. This indicates that characteristics such as the density of the original data are important factors that affect the accuracy of interpolation. In the case of sparse interpolation points, it is possible to consider fusing the interpolation results with different error patterns to improve their accuracy. This study provides a new idea for improving the accuracy of interpolation errors. Full article

Background: Knowing the spatial variation and predictors of women having sole autonomy over their healthcare decisions is crucial to design site-specific interventions. This study examined how women’s sole autonomy over their healthcare choices varies geographically and what factors influence this autonomy among Bangladeshi women of childbearing age. Methods: Data were obtained from the Bangladesh Demographic and Health Survey (BDHS) 2017–18. The final analysis included data from a total of 18,890 (weighted) women. Spatial distribution, hot spot analysis, ordinary Kriging interpolation, and multilevel multinomial regression analysis were employed. Results: The study found that approximately one in ten women (9.62%) exercised complete autonomy in making decisions about their healthcare. Spatial analysis revealed a significant clustering pattern in this autonomy (Moran’s I = 0.234, p < 0.001). Notably, three divisions—Barisal, Chittagong, and Sylhet—emerged as hot spots where women were more likely to have sole autonomy over their healthcare choices. In contrast, the cold spots (poor level of sole healthcare autonomy by women) were mainly identified in Mymensingh and Rangpur divisions. Women in the age group of 25–49 years, who were highly educated, Muslim, urban residents, and had not given birth recently were more likely to have sole autonomy in making healthcare decisions for themselves. Conversely, women whose husbands were highly educated and employed, as well as those who were pregnant, were less likely to have sole autonomy over their healthcare choices. Conclusions: Since the spatial distribution was clustered, public health interventions should be planned to target the cold spot areas of women’s sole healthcare autonomy. In addition, significant predictors contributing to women’s sole healthcare autonomy must be emphasized while developing interventions to improve women’s empowerment toward healthcare decision-making. Full article

Unconventional oil and gas reservoirs are frequently developed using inclined and horizontal wells, leading to intricate multiphase flow patterns due to spatial asymmetry surrounding the wellbore and gravitational differentiation effects. Through the examination of water holdup imaging, the spatial arrangement of oil and water phases within the wellbore may be clearly depicted, yielding critical information for precisely assessing the ratios of oil and gas. This study employed No. 10 industrial white oil and tap water as fluid media, with measurements obtained using a resistive ring array tool (RAT) to evaluate its response properties over the wellbore cross-section. The data gathered throughout the trials were analyzed by two-dimensional interpolation imaging utilizing 2020 version MATLAB software. To enhance the analysis of water holdup distribution in the wellbore, three interpolation algorithms were utilized: Simple Linear Interpolation (SLI), Inverse Distance Weighting Interpolation (IDWI), and Ordinary Kriging Interpolation (OKI). The results indicated that RAT operates effectively in medium and low flow circumstances, correctly representing the real distribution of oil and water phases while yielding more dependable water holdup data. The SLI algorithm effectively delineates the oil-water interface during stratified flow of oil and water phases, rendering it the optimal algorithm for determining water holdup in standard flow patterns. Under DW/O&W and DO/W&W flow patterns, SLI continues to perform well; however, the accuracy of IDWI and OKI markedly enhances, with IDWI more effectively delineating the attributes of intricate mixed flow and more precisely representing the dynamic fluid distribution. Under DW/O and DO/W flow patterns, the OKI algorithm exhibits optimal performance in these intricate dispersed flow patterns. OKI more precisely represents the dynamic distribution of dispersed oil and water due to its capacity to simulate the spatial correlation of both phases, surpassing both SLI and IDWI. Full article

Analyzing geological profiles is of great importance for various applications such as natural resource management, environmental assessment, and mining engineering projects. This study presents a novel geostatistical approach for subsurface geological profile interpolation using a fractional kriging method enhanced by random forest regression. Using bedrock elevation data from 49 boreholes in a study area in southeast China, we first use random forest regression to predict and optimize variogram parameters. We then use the fractional kriging method to interpolate the data and analyze the variability. We also compare the proposed model with traditional methods, including linear regression, K-nearest neighbors, ordinary kriging, and fractional kriging, using cross-validation metrics. The results indicate that the proposed model reduces prediction errors and enhances spatial prediction reliability compared to other models. The MSE of the proposed model is 25% lower than that of ordinary kriging and 10% lower than that of fractional kriging. In addition, the execution time of the proposed model is slightly higher than other models. The findings suggest that the proposed model effectively captures complex subsurface spatial relationships, offering a reliable and precise solution for performing spatial interpolation tasks. Full article

This study evaluated the desertification vulnerability of an Anatolian black pine forest in Türkiye using the Environmental Sensitivity Area Index (ESAI). Desertification Risk (DR) and ESAI values were calculated for 90 sampling plots, incorporating key indicators such as vegetation cover, soil depth, rock fragment presence, soil texture, slope gradient, parent material, mean annual precipitation, aridity index, land use intensity, and policy enforcement. These indicators were processed through the Desertification Indicator System for Mediterranean Europe (DIS4ME). Spatial patterns of DR and ESAI were analysed using semivariograms and Kriging-interpolated maps. The mean DR (4.850; range = 2.310–8.090) and ESAI (1.46; range = 1.390–1.580) values indicated significant vulnerability to desertification. DR showed moderate spatial dependence, while ESAI exhibited strong spatial dependence. Ordinary kriging maps revealed critical desertification hotspots within the forest. ESAI values varied with soil organic matter (SOM) content, which was moderately and significantly correlated with ESAI (n = 90, r = −0.58, p < 0.01). These findings provide actionable insights for sustainable land management. Interventions such as improving SOM content through afforestation, enhancing soil conservation practices, and promoting sustainable water use are critical to mitigating desertification and fostering ecosystem resilience. This study identifies high-risk areas and demonstrates how DR and ESAI can guide targeted strategies to restore degraded lands and ensure forest sustainability. This aligns with SDG 15 (Life on Land), which emphasizes the need to combat desertification, restore degraded ecosystems, and promote the sustainable management of forests. Integrating ESAI into regional policy planning highlights its potential as a practical tool for achieving long-term environmental and socioeconomic sustainability in vulnerable forest ecosystems like those in Türkiye. Full article

Soil nutrients are essential for plant survival, especially in karst regions where soil erosion is a significant threat, leading to ecosystem degradation. Rocks exposed in these areas contribute to fragmented soil coverage and the complex spatial distribution of soil nutrients, hindering vegetation recovery. In this study, we collected 60 soil samples (0–30 cm deep) from a typical rocky desertification slope. Classical statistics and geostatistics were used to assess the spatial variability of the following key soil properties: soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), and total potassium (TK). The study mapped a continuous surface of soil nutrients using the ordinary kriging method to analyze the spatial variability of the karst slope. The results showed that, except for the bulk density and porosity, which showed little variation, the other soil characteristics had moderate to high levels of variability. The SOC, TN, and TP levels decreased with soil depth, while the TK content increased with soil depth. Each soil layer has strong spatial autocorrelation in its SOC. The variability of TP and TK decreases with soil depth, indicating strong spatial autocorrelation. In the 0–10 cm soil layer, the SOC displays the highest level of continuity, with the TN exhibiting a higher level of variability compared to the other nutrients. Within the 10–20 cm soil layer, the SOC, TN, TP, and TK all exhibit strong spatial autocorrelation. Moving to the 20–30 cm soil layer, the structural variability of SOC is the most pronounced. The correlation between soil nutrients and other soil properties was not strong, with only a cumulative explanatory power of 11.81% in the first two axes of a redundancy analysis (RDA). Among them, the bulk density and silt content had a significant impact on soil nutrients. Studying the spatial variability of soil nutrients in rocky desertification areas is crucial for improving soil quality and promoting vegetation restoration. Full article

Assessing and mapping the geographical variation of soil properties is essential for precision agriculture to maintain the sustainability of the soil and plants. This study was conducted in El-Ismaillia Governorate in Egypt (arid zones), to establish site-specific management zones utilizing certain soil parameters in the study area. The goal of the study is to map out the variability of some soil properties. One hundred georeferenced soil profiles were gathered from the study area using a standard grid pattern of 400 × 400 m. Soil parameters such as pH, soil salinity (EC), soil organic carbon (SOC), calcium carbonate (CaCO₃), gravel, and soil-available micronutrients (Cu, Zn, Mn, and Fe) were determined. After the data were normalized, the soil characteristics were described and their geographical variability distribution was shown using classical and geostatistical statistics. The geographic variation of soil properties was analyzed using semivariogram models, and the associated maps were generated using the ordinary co-Kriging technique. The findings showed notable differences in soil properties across the study area. Statistical analysis of soil chemical properties showed that soil EC and pH have the highest and lowest coefficient of variation (CV), with a CV of 110.05 and 4.80%, respectively. At the same time Cu and Fe had the highest and lowest CV among the soil micronutrients, with a CV of 171.43 and 71.43%, respectively. Regarding the physical properties, clay and sand were the highest and lowest CV, with a CV of 177.01 and 9.97%, respectively. Moreover, the finest models for the examined soil attributes were determined to be exponential, spherical, K-Bessel, and Gaussian semivariogram models. The selected semivariogram models are the most suitable for mapping and estimating the spatial distribution surfaces of the investigated soil parameters, as indicated by the cross-validation findings. The results demonstrated that while Fe, Cu, Zn, gravel, silt, and sand suggested a weak spatial dependence, the soil variables under investigation had a moderate spatial dependence. The findings showed that there are three site- specific management zones in the investigated area. SSMZs were classified into three zones, namely high management zone (I) with an area 123.32 ha (7.09%), moderate management zone (II) with an area 1365.61ha (78.49%), and low management zone (III) with an area 250.8162 ha (14.42%). The majority of the researched area is included in the second site zone, which represents regions with low productivity. Decision-makers can identify locations with the finest, moderate, and poorest soil quality by using the spatial distribution maps that are produced, which can also help in understanding how each feature influences plant development. The results showed that geostatistical analysis is a reliable method for evaluating and forecasting the spatial correlations between soil properties. Full article

To effectively manage the sustainable urban development of cities, it is crucial to quickly understand the geological and geotechnical attributes of the underground. Carrying out such studies entails significant investments and focused reconnaissance efforts, which might not align seamlessly with large-scale territorial planning initiatives within a city accommodating more than 3 million inhabitants, like Casablanca in Morocco. Additionally, various specific investigations have been conducted by municipal authorities in recent times. The primary aim of this study is to furnish city managers and planners with a tool for informed decision-making, enabling them to explore the geological and geotechnical properties of soil foundations using Geographic Information Systems (GISs) and geostatistics. This database, initially intended for utilization by developers and construction engineers, stands to economize a substantial amount of time and resources. During the urban planning of cities and prior to determining land usage (five- or seven-floor structures), comprehending the mechanical traits (bearing capacity, water levels, etc.) of the soil is crucial. To this end, geological and geotechnical maps, along with a collection of 100 surveys, were gathered and incorporated into a GIS system. These diverse data sources converged to reveal that the underlying composition of the surveyed area comprises silts, calcarenites, marls, graywackes, and siltstones. These formations are attributed to the Middle Cambrian and the Holocene epochs. The resultant geotechnical findings were integrated into the GIS and subjected to interpolation using ordinary kriging. This procedure yielded two distinct maps: one illustrating bearing capacity and the other depicting the substratum. The bearing capacity of the soil in the study zone is rated as moderate, fluctuating between two and four bars. The depth of the foundation remains relatively shallow, ranging from 0.8 m to 4.5 m. The outcomes are highly promising, affirming that the soil in Casablanca boasts commendable geotechnical attributes capable of enduring substantial loads and stresses. Consequently, redirecting future urban planning in the region toward vertical expansion seems judicious, safeguarding Casablanca’s remaining green spaces and the small agricultural belt. The results of this work help to better plan the urban development of the city of Casablanca in a smarter way, thus preserving space, agriculture, and the environment while promoting sustainability. In addition, the databases and maps created through this paper aim for a balanced financial management of city expenditures in urban planning. Full article

Soil fertility is a critical factor in agricultural production, directly impacting crop growth, yield, and quality. To achieve precise agricultural management, accurate spatial interpolation of soil parameters is essential. This study developed a new interpolation prediction framework that combines Radial Basis Function (RBF) neural networks with Inverse Distance Weighting (IDW), termed the IDW-RBFNN. This framework initially uses the IDW method to apply preliminary weights based on distance to the data points, which are then used as input for the RBF neural network to form a training dataset. Subsequently, the RBF neural network further trains on these data to refine the interpolation results, achieving more precise spatial data interpolation. We compared the interpolation prediction accuracy of the IDW-RBFNN framework with ordinary Kriging (OK) and RBF methods under three different parameter settings. Ultimately, the IDW-RBFNN demonstrated lower error rates in terms of RMSE and MRE compared to direct RBF interpolation methods when adjusting settings based on different power values, even with a fixed number of data samples. As the sample size decreases, the interpolation accuracy of OK and RBF methods is significantly affected, while the error of IDW-RBFNN remains relatively low. Considering both interpolation accuracy and resource limitations, we recommend using the IDW-RBFNN method (p = 2) with at least 60 samples as the minimum sampling density to ensure high interpolation accuracy under resource constraints. Our method overcomes limitations of existing approaches that use fixed steady-state distance decay parameters, providing an effective tool for soil fertility monitoring in delta regions. Full article

Essential soil nutrients are dynamic in nature and require timely management in farmers’ fields. Accurate prediction of the spatial distribution of soil nutrients using a suitable sampling density is a prerequisite for improving the practical utility of spatial soil fertility maps. However, practical research is required to address the challenge of selecting an optimal sampling density that is both cost-effective and accurate for preparing digital soil nutrient maps across regional extents. This study examines the impact of sampling density on spatial prediction accuracy for a range of soil fertility parameters over a regional extent of 8303 km² located in eastern India. Surface soil samples were collected from 1024 sample points. The performance of six levels of sampling densities for spatial prediction of 14 soil properties was compared using ordinary kriging. From the sample points, randomization was used to select 224 points for validation and the remaining 800 for calibration. Goodness-of-fit for the semi-variograms was evaluated by R² of model fit. Lin’s concordance correlation coefficient (CCC) and root mean square error (RMSE) were evaluated through independent validation as spatial prediction accuracy parameters. Results show that the impact of sampling density on prediction accuracy was unique for each soil property. As a common trend, R² of model fit and CCC scores improved, and RMSE values declined with the increasing sampling density for all soil properties. On the other hand, the rate of gain in the accuracy metrics with each increment in the sampling density gradually decreased and ultimately plateaued. This indicates that there exists a sampling density threshold beyond which the extra effort on additional sampling adds less to the spatial prediction accuracy. The findings of this study provide a valuable reference for optimizing soil nutrient mapping across regional extents. Full article