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The main objective of the current study was to use seven lots in Hartford, CT that are planned for community reuse to determine the optimal sampling density that allows for the detection of hotspots of lead pollution while limiting the labor of the sampling process. The sampling density was investigated using soil Pb measured by in situ X-ray Fluorescence as the indicator to evaluate soil health, with a new threshold of 200-mg/kg proposed by the USEPA in January of 2024. Even though this study takes place in an urban setting, where the new USEPA policy requires the use of a 100-mg/kg threshold for Pb due to the fact that there are other identifiable sources of the contaminant, only the 200-mg/kg threshold is discussed because it is evident from the analysis that compliance of a 100 mg/kg threshold in urban plots is highly unlikely (five out of seven sites would require complete site excavation prior to reuse). Using the inverse distance weighted geospatial interpolation of in situ pXRF determined lead measurements, grid sampling resolutions of 3-m, 4-m, 5-m, 6-m, 8-m, 10-m, and 12-m were compared. Ultimately, the case study finds that the largest grid resolution that can be implemented for soil screening to maintain hotspots of pollution to properly inform soil management decisions is a 6-m grid, or a density of approximately 1/36-m2. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

How to acquire the exact center of a circular sample is an essential task in object recognition. Present algorithms suffer from the high time consumption and low precision. To tackle these issues, we propose a novel circle center location algorithm based on sample density and adaptive thresholding. After obtaining circular contours through image pre-processing, these contours were segmented using a grid method to obtain the required coordinates. Based on the principle of three points forming a circle, a data set containing a large number of samples with circle center coordinates was constructed. It was highly probable that these circle center samples would fall within the near neighborhood of the actual circle center coordinates. Subsequently, an adaptive bandwidth fast Gaussian kernel was introduced to address the issue of sample point weighting. The mean shift clustering algorithm was employed to compute the optimal solution for the density of candidate circle center sample data. The final optimal center location was obtained by an iteration algorithm. Experimental results demonstrate that in the presence of interference, the average positioning error of this circle center localization algorithm is 0.051 pixels. Its localization accuracy is 64.1 % higher than the Hough transform and 86.4 % higher than the circle fitting algorithm. [ABSTRACT FROM AUTHOR]

The mapping of soils in Africa is at least a century old. We currently have access to various maps depicting mapping units locally and for the continent. In the past two decades, there has been a growing interest in alternatives for generating soil maps through digital soil mapping (DSM) techniques. There are, however, numerous challenges pertaining to the implementation of DSM in Africa, such as the unavailability of appropriate covariates, age and positional error in the measurements, low sampling density, and spatial clustering of the soil data used to fit and validate the models. This review aims to investigate the current state of DSM in Africa, identify challenges specific to implementing DSM in Africa and the ways it has been solved in the literature. We found that nearly half of African countries had an existing digital soil map covering either a local or national area, and that most studies were performed at a local extent. Soil carbon was the most common property under study, whereas soil hydraulic variables were seldom reported. Nearly all studies performed mapping for the topsoil up to 30 cm and calculated validation statistics using existing datasets but without collecting a post-mapping probability sample. Few studies (i.e., 11%) reported an estimate of map uncertainty. Half of the studies had in mind a downstream application (e.g., soil fertility assessment) in the map generation. We further correlated the area of study and sampling density and found a strong negative relationship. About 30% of the studies relied on legacy soil datasets and had a lack of sufficient spatial coverage of their area of study. From this review, we highlight some research opportunities and suggest improvements in the current methodologies. Future research should focus on capacity building in DSM, new data collection, and legacy data rescue. New initiatives, that should be initiated and led from within the continent, could support the long-term monitoring of soils and updating of soil information systems while ensuring their contextualised usability. This pairs with better delivery of existing DSM studies to stakeholders and the generation of a value-added proposition to governmental institutions.

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.

How to acquire the exact center of a circular sample is an essential task in object recognition. Present algorithms suffer from the high time consumption and low precision. To tackle these issues, we propose a novel circle center location algorithm based on sample density and adaptive thresholding. After obtaining circular contours through image pre-processing, these contours were segmented using a grid method to obtain the required coordinates. Based on the principle of three points forming a circle, a data set containing a large number of samples with circle center coordinates was constructed. It was highly probable that these circle center samples would fall within the near neighborhood of the actual circle center coordinates. Subsequently, an adaptive bandwidth fast Gaussian kernel was introduced to address the issue of sample point weighting. The mean shift clustering algorithm was employed to compute the optimal solution for the density of candidate circle center sample data. The final optimal center location was obtained by an iteration algorithm. Experimental results demonstrate that in the presence of interference, the average positioning error of this circle center localization algorithm is 0.051 pixels. Its localization accuracy is 64.1% higher than the Hough transform and 86.4% higher than the circle fitting algorithm.

Soils are at the crossroads of many existential issues that humanity is currently facing. Soils are a finite resource that is under threat, mainly due to human pressure. There is an urgent need to map and monitor them at field, regional, and global scales in order to improve their management and prevent their degradation. This remains a challenge due to the high and often complex spatial variability inherent to soils. Over the last four decades, major research efforts in the field of pedometrics have led to the development of methods allowing to capture the complex nature of soils. As a result, digital soil mapping (DSM) approaches have been developed for quantifying soils in space and time. DSM and monitoring have become operational thanks to the harmonization of soil databases, advances in spatial modeling and machine learning, and the increasing availability of spatiotemporal covariates, including the exponential increase in freely available remote sensing (RS) data. The latter boosted research in DSM, allowing the mapping of soils at high resolution and assessing the changes through time. We present a review of the main contributions and developments of French (inter)national research, which has a long history in both RS and DSM. Thanks to the French SPOT satellite constellation that started in the early 1980s, the French RS and soil research communities have pioneered DSM using remote sensing. This review describes the data, tools, and methods using RS imagery to support the spatial predictions of a wide range of soil properties and discusses their pros and cons. The review demonstrates that RS data are frequently used in soil mapping (i) by considering them as a substitute for analytical measurements, or (ii) by considering them as covariates related to the controlling factors of soil formation and evolution. It further highlights the great potential of RS imagery to improve DSM, and provides an overview of the main challenges and prospects related to digital soil mapping and future sensors. This opens up broad prospects for the use of RS for DSM and natural resource monitoring. [ABSTRACT FROM AUTHOR]

As far as the knowledge of the seabed is concerned, both for safe navigation and for scientific research, 3D models, particularly digital bathymetric models (DBMs), are nowadays of fundamental importance. This work aimed to evaluate the quality of DBMs according to the interpolation methods applied to obtain grid format 3D surfaces from scattered sample points. Other complementary factors affecting DBM vertical accuracy, such as seabed morphological complexity and surveyed points sampling density, were also analyzed by using a factorial ANOVA experimental design. The experiments were performed on a multibeam dataset provided by the Italian Navy Hydrographic Institute (IIM) with an original resolution of 1 m × 1 m grid spacing, covering a surface of 0.24 km2. Six different sectors comprising different seabed morphologies were investigated. Eight sampling densities were randomly extracted from every sector, each with four repetitions. Finally, four different interpolation methods were tested, including: radial basis multiquadric function (RBMF), ordinary kriging (OK), universal kriging (UK) and Gaussian Markov random fields (GMRF). The results demonstrated that both RBMF and OK produced very accurate DBM in areas characterized by low levels of seabed ruggedness at sampling densities of only 0.0128 points/m2 (equivalent grid spacing of 8.84 m). In contrast, a higher density of 0.1024 points/m2 (3.13 m grid spacing) was required to produce accurate DBM in areas with more complex seabed topography. On the other hand, UK and GMRF were strongly influenced by morphology and sampling density, yielding higher vertical random errors and more prone to slightly overestimate seabed depths. In addition, sampling density and morphology were the factors that most influenced the vertical accuracy of the interpolated DBM. In this sense, the highly statistically significant influence of the interaction between sampling density and morphology on the vertical accuracy of the interpolated DBM confirms the need to perform a preliminary analysis of seabed morphological complexity in order to increase, if necessary, the number of surveyed points in cases of complex morphologies. [ABSTRACT FROM AUTHOR]

High fidelity gridded temperature datasets are difficult to obtain for areas with sparse coverage of meteorological stations given that sparsity of stations is known to introduce uncertainty in the interpolation of climatic variables generally. Inspired by their potential for optimal results especially for small sample datasets, we assessed and compared the accuracy of interpolation results of Empirical Bayesian Kriging (EBK) and EBK-Regression Prediction (EBKRP) spatial prediction techniques under varying sampling density scenarios, using monthly maximum temperature normals (1991–2020) for the entire area of Sweden. The objectives of the study were to understand how EBK and EBKRP interpolation techniques perform in different sampling density scenarios and particularly in a sparse data setting, and the possible difference in the prediction accuracy between the two techniques. The 708 sampled stations obtained from the historical climatology database of the Swedish Meteorological and Hydrological Institute (SMHI) were split into seven sampling density subsets, ranging from 1 sample per 63,614 km2 to 1 sample per 634,350 km2 and representing both low and high sampling density scenarios. EBK interpolation technique was implemented using temperature data while land use land cover (LULC) and digital elevation model (DEM) were used as temperature covariates for the EBKRP interpolation models. The prediction accuracy assessment was based on five robust prediction performance indicators – mean error, mean absolute error, mean square error, root mean square error and Pearson correlation (R) – obtained from independent validation/cross-validation operations. Prediction accuracy was found to be generally positively related to sampling density, and sampling density accounted for about 85%–87% of interpolation accuracy for both EBK and EBKRP techniques. Although sampling density increased linearly, the rate of change in accuracy from one sampling density step to the next was not particularly proportional. For equivalent sampling density set-ups, EBKRP consistently outperformed EBK in all the accuracy metrics and EBKRP proved to be approximately 40% better than EBK. However, the two interpolation techniques produced generally low prediction biases for all the sampling density scenarios investigated. Our study suggests that potential effects of low sampling density and non-stationarity of temperature data can be significantly reduced by applying EBK but especially EBKRP when coupled with relevant covariates. This is especially true for continuous and slowly varying phenomena such as temperature and similar variables.

In general, the main criteria for determining the optimal soil sampling grid are the configuration of the relief and the soil properties of a particular land parcel. Soil sampling for agrochemical testing of soils is regulated by Slovak legislation, for the vineyard the density of the sampling grid is determined as one sample per two to four hectares of land, what means six to nine samples for the selected research area of 17 ha. This density is several times lower compared to the results of most scientific papers, therefore 22 samples (1 sample / 0.77 ha) were taken to determine phosphorus and magnesium and 73 samples (1 sample / 0.23 ha) to determine the carbon and nitrogen content. Subsequently, 12 dilute networks were created from the concentrated sampling network, which entered into interpolation. By comparing the created rasters, we determined the value of spatial loss of information on the state of nutrients in the soil, which made it possible to determine the optimal density of soil sampling for the needs of precision agriculture, considering the financial complexity of laboratory analyses. We determined the most suitable 10-point grid (1 sample / 1.7 ha). The research was carried out on a model vineyard in Modrany (Komárno district). [ABSTRACT FROM AUTHOR]

The mapping of soils in Africa is at least a century old. We currently have access to various maps depicting mapping units locally and for the continent. In the past two decades, there has been a growing interest in alternatives for generating soil maps through digital soil mapping (DSM) techniques. There are, however, numerous challenges pertaining to the implementation of DSM in Africa, such as the unavailability of appropriate covariates, age and positional error in the measurements, low sampling density, and spatial clustering of the soil data used to fit and validate the models. This review aims to investigate the current state of DSM in Africa, identify challenges specific to implementing DSM in Africa and the ways it has been solved in the literature. We found that nearly half of African countries had an existing digital soil map covering either a local or national area, and that most studies were performed at a local extent. Soil carbon was the most common property under study, whereas soil hydraulic variables were seldom reported. Nearly all studies performed mapping for the topsoil up to 30 cm and calculated validation statistics using existing datasets but without collecting a post-mapping probability sample. Few studies (i.e., 11%) reported an estimate of map uncertainty. Half of the studies had in mind a downstream application (e.g., soil fertility assessment) in the map generation. We further correlated the area of study and sampling density and found a strong negative relationship. About 30% of the studies relied on legacy soil datasets and had a lack of sufficient spatial coverage of their area of study. From this review, we highlight some research opportunities and suggest improvements in the current methodologies. Future research should focus on capacity building in DSM, new data collection, and legacy data rescue. New initiatives, that should be initiated and led from within the continent, could support the long-term monitoring of soils and updating of soil information systems while ensuring their contextualised usability. This pairs with better delivery of existing DSM studies to stakeholders and the generation of a value-added proposition to governmental institutions. • Half of African countries have an existing digital soil mapping study. • There are numerous challenges for generating soil maps in Africa. • Local initiatives for a sustainable soil data collection is required. • More efforts should be invested in capacity building and soil monitoring systems. • Delivery of detailed soil information to stakeholders is insufficient. [ABSTRACT FROM AUTHOR]

Soils are at the crossroads of many existential issues that humanity is currently facing. Soils are a finite resource that is under threat, mainly due to human pressure. There is an urgent need to map and monitor them at field, regional, and global scales in order to improve their management and prevent their degradation. This remains a challenge due to the high and often complex spatial variability inherent to soils. Over the last four decades, major research efforts in the field of pedometrics have led to the development of methods allowing to capture the complex nature of soils. As a result, digital soil mapping (DSM) approaches have been developed for quantifying soils in space and time. DSM and monitoring have become operational thanks to the harmonization of soil databases, advances in spatial modeling and machine learning, and the increasing availability of spatiotemporal covariates, including the exponential increase in freely available remote sensing (RS) data. The latter boosted research in DSM, allowing the mapping of soils at high resolution and assessing the changes through time. We present a review of the main contributions and developments of French (inter)national research, which has a long history in both RS and DSM. Thanks to the French SPOT satellite constellation that started in the early 1980s, the French RS and soil research communities have pioneered DSM using remote sensing. This review describes the data, tools, and methods using RS imagery to support the spatial predictions of a wide range of soil properties and discusses their pros and cons. The review demonstrates that RS data are frequently used in soil mapping (i) by considering them as a substitute for analytical measurements, or (ii) by considering them as covariates related to the controlling factors of soil formation and evolution. It further highlights the great potential of RS imagery to improve DSM, and provides an overview of the main challenges and prospects related to digital soil mapping and future sensors. This opens up broad prospects for the use of RS for DSM and natural resource monitoring.

Abstract In landscape genetics, it is largely unknown how choices regarding sampling density and study area size impact inferences upon which habitat features impede vs. facilitate gene flow. While it is recommended that sampling locations be spaced no further apart than the average individual's dispersal distance, for low‐mobility species, this could lead to a challenging number of sampling locations, or an unrepresentative study area. We assessed the effects of sampling density and study area size on landscape genetic inferences for a dispersal‐limited amphibian, Plethodon mississippi, via analysis of nested datasets. Microsatellite‐based genetic distances among individuals were divided into three datasets representing sparse sampling across a large study area, dense sampling across a small study area, or sparse sampling across the same small study area. These datasets were a proxy for gene flow (i.e., the response variable) in maximum‐likelihood population effects models that assessed the nature and strength of their relationship with each of five land‐use classes (i.e., potential predictor variables). Comparisons of outcomes were based on the rank order of effect, sign of effect (i.e., gene flow resistance vs. facilitation), spatial scale of effect, and functional relationship with gene flow. The best‐fit model for each dataset had the same sign of effect for hardwood forests, manmade structures, and pine forests, indicating the impacts of these land‐use classes on dispersal and gene flow in P. mississippi are robust to sampling scheme. Contrasting sampling densities led to a different inferred functional relationship between agricultural areas and gene flow. Study area size appeared to influence the scale of effect of manmade structures and the sign of effect of pine forests. Our findings provided evidence for an influence of sampling density, study area size, and sampling effort upon inferences. Accordingly, we recommend iterative subsampling of empirical datasets and continued investigation into the sensitivities of landscape genetic analyses using simulations.

As far as the knowledge of the seabed is concerned, both for safe navigation and for scientific research, 3D models, particularly digital bathymetric models (DBMs), are nowadays of fundamental importance. This work aimed to evaluate the quality of DBMs according to the interpolation methods applied to obtain grid format 3D surfaces from scattered sample points. Other complementary factors affecting DBM vertical accuracy, such as seabed morphological complexity and surveyed points sampling density, were also analyzed by using a factorial ANOVA experimental design. The experiments were performed on a multibeam dataset provided by the Italian Navy Hydrographic Institute (IIM) with an original resolution of 1 m × 1 m grid spacing, covering a surface of 0.24 km2. Six different sectors comprising different seabed morphologies were investigated. Eight sampling densities were randomly extracted from every sector, each with four repetitions. Finally, four different interpolation methods were tested, including: radial basis multiquadric function (RBMF), ordinary kriging (OK), universal kriging (UK) and Gaussian Markov random fields (GMRF). The results demonstrated that both RBMF and OK produced very accurate DBM in areas characterized by low levels of seabed ruggedness at sampling densities of only 0.0128 points/m2 (equivalent grid spacing of 8.84 m). In contrast, a higher density of 0.1024 points/m2 (3.13 m grid spacing) was required to produce accurate DBM in areas with more complex seabed topography. On the other hand, UK and GMRF were strongly influenced by morphology and sampling density, yielding higher vertical random errors and more prone to slightly overestimate seabed depths. In addition, sampling density and morphology were the factors that most influenced the vertical accuracy of the interpolated DBM. In this sense, the highly statistically significant influence of the interaction between sampling density and morphology on the vertical accuracy of the interpolated DBM confirms the need to perform a preliminary analysis of seabed morphological complexity in order to increase, if necessary, the number of surveyed points in cases of complex morphologies.

In landscape genetics, it is largely unknown how choices regarding sampling density and study area size impact inferences upon which habitat features impede vs. facilitate gene flow. While it is recommended that sampling locations be spaced no further apart than the average individual's dispersal distance, for low‐mobility species, this could lead to a challenging number of sampling locations, or an unrepresentative study area. We assessed the effects of sampling density and study area size on landscape genetic inferences for a dispersal‐limited amphibian, Plethodon mississippi, via analysis of nested datasets. Microsatellite‐based genetic distances among individuals were divided into three datasets representing sparse sampling across a large study area, dense sampling across a small study area, or sparse sampling across the same small study area. These datasets were a proxy for gene flow (i.e., the response variable) in maximum‐likelihood population effects models that assessed the nature and strength of their relationship with each of five land‐use classes (i.e., potential predictor variables). Comparisons of outcomes were based on the rank order of effect, sign of effect (i.e., gene flow resistance vs. facilitation), spatial scale of effect, and functional relationship with gene flow. The best‐fit model for each dataset had the same sign of effect for hardwood forests, manmade structures, and pine forests, indicating the impacts of these land‐use classes on dispersal and gene flow in P. mississippi are robust to sampling scheme. Contrasting sampling densities led to a different inferred functional relationship between agricultural areas and gene flow. Study area size appeared to influence the scale of effect of manmade structures and the sign of effect of pine forests. Our findings provided evidence for an influence of sampling density, study area size, and sampling effort upon inferences. Accordingly, we recommend iterative subsampling of empirical datasets and continued investigation into the sensitivities of landscape genetic analyses using simulations. [ABSTRACT FROM AUTHOR]

Copyright of East China Geology / Huadong dizhi is the property of Editorial Department of East China Geology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Convolution filters play important roles in improving the abstraction ability of segmentation networks due to its method of fusing features from all input channels. Such a method pays little attention to protecting the channel-wise features which is important in preserving the detail features in the final result. Though a few works has discussed this problem, the function of channel-wise features is still not sufficiently exploited. For investigating the beneficial effect of channel-wise feature on improving details representation ability, we design a Hybrid Pyramid Pooling (HPP) structure to explicitly exploit the channel-wise features. Through utilizing different scales of depthwise dilated convolution, segmentation networks can fully utilize the superiority of channel-wise features to improve the details representation ability. For deeply developing the advantages of the channel-wisely calculated features by HPP, we design another Stacked Pyramid Structure (SPS), which contains stacked pyramid pooling structures. The SPS distributes sufficient sampling points in each finely divided sampling area, and fuses features from a large range of receptive fields to produce rich feature representation. Finally, the combination of HPP and SPS can not only maintains both the advantages of features calculated from a particular channel and all input channels, but also provides sufficiently enriched receptive fields for depicting details in the final result. [ABSTRACT FROM AUTHOR]

In this paper, we consider an infinite-dimensional phase retrieval problem to reconstruct real-valued signals living in a shift-invariant space from their phaseless samples taken either on the whole line or on a discrete set with finite sampling density. We characterize all phase retrievable signals in a real-valued shift-invariant space using their nonseparability. For nonseparable signals generated by some function with support length L , we show that they can be well approximated, up to a sign, from their noisy phaseless samples taken on a discrete set with sampling density 2 L − 1. In this paper, we also propose an algorithm with linear computational complexity to reconstruct nonseparable signals in a shift-invariant space from their phaseless samples corrupted by bounded noises. [ABSTRACT FROM AUTHOR]

The tool setting technology by laser diffraction is a method of evaluating the tool setting precision by detecting the distance between the light intensity peak points of the first order diffraction fringes formed on the charge-coupled device (CCD). In order to solve the problem that the planar array CCD has low detection accuracy due to the limitation of sampling density during conventional imaging. The sampling density of the tool setting diffraction image is improved by using the planar array CCD staggered imaging technique in this paper, detailed methods are as follows: The single planar array CCD is integrally shifted by the combination of the nano-lifting platform and the high precision two-dimensional (2-D) translation stage, is used to image the tool setting gap with a certain object distance. And then, four diffraction-strip images with complementary information are obtained, which we can use to stitch the image information according to the translocation relationship between each image, and finally a higher resolution diffraction image is obtained. Through detecting the distance between the light intensity peak points of the first order diffraction fringes of higher resolution diffraction image, the detection precision of the tool setting gap of laser diffraction is improved 50 to 70 µm. The results indicate that the detection precision of tool setting gap can be improved effectively by increasing the image sampling density with the planar CCD staggered imaging technology. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Sugarcane productivity is highly variable across space, and knowledge about the spatial distribution of soil physical attributes is important to guide management practices and evaluate the effects of agricultural operations on productivity. Thus, the objective of this study was to determine the best sampling density to represent the physical attributes of an Ultisol Typic Hapludult, to determine the spatial variability of these physical attributes, and to correlate them with sugarcane productivity. A sampling grid was delimited in a sugarcane crop in its first cycle, where 32 samples were collected that were spaced 7 m apart. The samples were collected undisturbed for soil density, total porosity, macroporosity and microporosity determination. In addition, disturbed samples were also collected to determine gravimetric water content. A penetrometer was used to evaluate the soil mechanical resistance. Sugarcane productivity was measured in the same sites. Descriptive statistics and geostatistical analysis were applied to evaluate data variability and correlation between sugarcane productivity and soil physical attributes. The physical attributes of soil: penetration resistance, moisture, total porosity, macroporosity, microporosity, soil density and sugarcane productivity showed spatial dependence, and fit to spherical and exponential mathematical models. Kriging maps identified homogeneous and heterogeneous regions, indicating that the geostatistical techniques were useful for geospatial characterization. The minimum sampling density to estimate sugarcane productivity was 14 samples per hectare and was dependent on penetration resistance and total soil porosity. [ABSTRACT FROM AUTHOR]

Sampling in shift-invariant spaces is a realistic model for signals with smooth spectrum. In this paper, we consider phaseless sampling and reconstruction of real-valued signals in a high-dimensional shift-invariant space from their magnitude measurements on the whole Euclidean space and from their phaseless samples taken on a discrete set with finite sampling density. The determination of a signal in a shift-invariant space, up to a sign, by its magnitude measurements on the whole Euclidean space has been shown in the literature to be equivalent to its nonseparability. In this paper, we introduce an undirected graph associated with the signal in a shift-invariant space and use connectivity of the graph to characterize nonseparability of the signal. Under the local complement property assumption on a shift-invariant space, we find a discrete set with finite sampling density such that nonseparable signals in the shift-invariant space can be reconstructed in a stable way from their phaseless samples taken on that set. In this paper, we also propose a reconstruction algorithm which provides an approximation to the original signal when its noisy phaseless samples are available only. Finally, numerical simulations are performed to demonstrate the robustness of the proposed algorithm to reconstruct box spline signals from their noisy phaseless samples. [ABSTRACT FROM AUTHOR]

A variety of factors contribute to the proper and efficient evaluation of geochemical programs including sampling medium, sample size and sample fraction, sampling density, digestion procedure, and analytical techniques. Among the aforementioned factors, sampling density is of great importance, affecting project ability to detect anomalous sites, which are either economically viable or environmentally hazardous. It is quite critical to have a sufficient number of samples to delineate geochemical anomalies or baselines and to reveal the relative variations in elemental concentrations in a study area. The resolution of the subsequent maps obtained by sampling greatly depends on sampling density. While a large number of samples may generate redundant data, a small number of samples smooth out the patterns and may completely miss anomalies. This review provides a platform for applied geochemists who desire to design a sampling scheme for a regional geochemical program and do not have the financial resources to perform an orientation survey, which will give them the necessary information for designing, according to project objectives, a cost-effective regional geochemical survey for their study area. It is advisable to integrate during the desktop study all available information, such as geological, structural, geophysical, remote sensing data and topographical data by using different data fusion algorithms to arrive at a sampling density fit-for-purpose of the target area. This in turn facilitates an effective spatial positioning of sample locations. Quantitative analyses of the efficiency of different sampling densities for delineating geochemical patterns have not been well documented, warranting more attention and research. [ABSTRACT FROM AUTHOR]

Spatial interpolation methods are widely used in various fields and have been studied by several scholars with one or a few specific sampling datasets that do not reflect the complexity of the spatial characteristics and lead to conclusions that cannot be widely applied. In this paper, three factors that affect the accuracy of interpolation have been considered, i.e., sampling density, sampling mode, and sampling location. We studied the inverse distance weighted (IDW), regular spline (RS), and ordinary kriging (OK) interpolation methods using 162 DEM datasets considering six sampling densities, nine terrain complexities, and three sampling modes. The experimental results show that, in selective sampling and combined sampling, the maximum absolute errors of interpolation methods rapidly increase and the estimated values are overestimated. In regular-grid sampling, the RS method has the highest interpolation accuracy, and IDW has the lowest interpolation accuracy. However, in both selective and combined sampling, the accuracy of the IDW method is significantly improved and the RS method performs worse. The OK method does not significantly change between the three sampling modes. The following conclusion can be obtained from the above analysis: the combined sampling mode is recommended for sampling, and more sampling points should be added in the ridges, valleys, and other complex terrain. The IDW method should not be used in the regular-grid sampling mode, but it has good performance in the selective sampling mode and combined sampling mode. However, the RS method shows the opposite phenomenon. The sampling dataset should be analyzed before using the OK method, which can select suitable models based on the analysis results of the sampling dataset.

There is currently limited understanding surrounding the spatial accuracy of soil amelioration advice as a function of sampling density at the sub-field scale. Consequently, soil-based decisions are often made using a data limiting approach, as the value proposition of soil data collection has not been well described. The work presented here investigates the spatial errors of gypsum and lime recommendations based on industry-standard blanket-rate and zone-based variable rate application, as well as the more advanced pedometric approaches – ordinary kriging (OK) and regression kriging (RK). All methods were tested at sampling densities between 0.1–3 samples/ha for a 108 ha broadacre site in central NSW, Australia. Whilst previous work has tested the effect of sampling density on the spatial predictive performance of OK and RK, here we assess prediction accuracy as the error associated with soil management decisions based on their results (i.e., the over- and under-application error of gypsum and lime applications) in conjunction with the RMSE of prediction for soil pH and exchangeable sodium percentage (ESP). The uncertainty of each method is also tested to observe the effect of random initialisation on predictive performance. Results indicated that RK provided superior spatial predictions across all sampling densities for the application of gypsum and lime, with a blanket-rate application providing the worse results, with over- and under-application errors exceeding 200 t and 300 t respectively for 40–60 cm treatment for the entire field. Interestingly, the spatial accuracy of amendment application increased to a sampling density of 0.5 samples/ha for RK, with minimal improvement thereafter, suggesting that meaningful soil amelioration advice can be attained proximal to this density.

Based on previous studies, the research methods and influencing factors of spatial variation of soil nutrients are summarized. It is concluded that the spatial variation of soil nutrients is studied generally by geostatistics methods, and the spatial distribution of nutrients is visually observed by using Kriging interpolation method. The influencing factors mainly include topography, sampling method, sampling spacing, sampling density and sampling scale. The influence of random sampling and grid sampling on interpolation is analyzed based on the specific conditions of the actual study area. The influence of sampling density and topography on the spatial variation of soil nutrients cannot be ignored, especially on available nutrients. When samples are collected in a large area (under a small and medium scale), the spatial variation of soil nutrients is large, and they have strong spatial autocorrelation; in a small area (namely under a large scale), the spatial variability of soil nutrients is small, and they have obvious spatial autocorrelation. This study can provide intuitive and convenient reference materials for the following researchers. [ABSTRACT FROM AUTHOR]

Sampling density significantly affects the estimation of soil organic matter (SOM) concentration because it influences the interpolation accuracy. High sampling density may ensure adequate estimation, but it is costly. Low number of samples may underrepresent spatial variation and generate unacceptable predictions. Identifying a reasonable sampling density is challenging, especially where the topography is complex and characterized by slope-rich terrain. Here we addressed this challenge by taking a large region of complex topography as study area. The region had a total area of 1.24 × 10 5  km 2 and can be separated into three typical landforms, namely, hill-mountain, valley-basin, and plain-platform. Out of 235,309 sampling sites, 188,247 were randomly selected as training sites, on which 20 sampling densities were designed and ordinary kriging was interpolated. The remaining 47,602 sites were used as testing sites to calculate the accuracies of SOM concentration predictions at different sampling densities in the entire region of complex topography and its various landforms. Overall, the prediction accuracy was positively correlated with the sampling density (R 2  ≥ 0.98). Specifically, with increasing sampling density, accuracy improved slowly at first then rapidly. However, the tipping point at which prediction accuracy significantly improved with the increases of sampling density varied among the areas. These sampling densities were 0.10, 0.11, 0.10, and 0.09 samples per hectare for the entire region, valley-basin, hill-mountain, and plain-platform, respectively. Further comparisons showed that valley-basin was the landform that had the best performance in interpolation accuracy, followed by hill-mountain, entire region and plain-platform. Their normalized root mean square error (NRMSE) values were 23.86%–28.91%, 24.22%–29.54%, 25.32%–30.77%, and 31.21%–37.76%, respectively. Moreover, interpolation accuracy was more sensitive to sampling density in simple topography (flat regions such as plain-platform) than in complex landforms (slope-rich terrains like hill-mountain, and valley-basin). These variations in the relationships between interpolation accuracy and sample density suggest that topography must be considered when designing a scientific sampling density. More importantly, when a high level of interpolation accuracy and low sampling costs are required in regions of similar complex topography, our findings may help optimize soil sampling density. [ABSTRACT FROM AUTHOR]

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Sampling density is an important parameter in different stages of exploration programs such as stream sedimentbased geochemical exploration. Some researchers have suggested the effects of different sampling densities in regional exploration. However, a few researchers have addressed the effect of stream sediment sampling density on anomaly delineation. This research aims at investigating the effect of sampling density in delineating ore-related geochemical signals in stream sediment exploration through sensitivity analysis. A dataset from northwestern Iran with known copper occurrences was employed, and the available data was systematically resampled into different proportions to simulate different sampling densities and analyzed using the true sample catchment basin approach. The results showed a positive correlation between the number of outlining known deposits and sampling density, implying that a higher sampling density increases the chance of outlining new potential zones. Moreover, when sampling density increases, the total size of the delimited anomalous area decreases. The estimated background values (Mj) for upstream lithologic units varies heavily with sampling density. This is critical as separating anomalous basins depends on Mj. Mj variation has been more emphasized for lithologies with high estimated background content, displaying heteroscedastic behavior. [ABSTRACT FROM AUTHOR]

The Universal Soil Loss Equation (USLE) is an empirical equation commonly applied to predict soil erosion throughout the world. In China, the Chinese Soil Loss Equation (CSLE) has been developed for estimating annual soil erosion by water on the basis of the USLE. It is proven that two factors (i.e., sampling density and extrapolation method) are important in the application of CSLE for soil erosion survey and evaluation. In this study, based on the SPOT 6 image (1.5 m resolution) and the 1:10,000 topographic map in Yishui county of the Yimeng Mountain Area (YMA), eastern China, two sampling densities (1% and 4%) were selected and three extrapolation methods (grid based calculation, direct extrapolation, and Kriging extrapolation based on sampling units) were adopted to compare their effect on the CSLE calculated soil erosion condition and status. Results showed that estimated soil erosion by direct extrapolation and Kriging extrapolation methods varies largely with sampling density. The discrepancy of the soil erosion area ratios calculated by the two methods are 6.5% and 7.7% under the 1% and 4% sampling density, and the relative discrepancies are 15.9% and 16.2%, respectively. But the grid calculation method is less affected by sampling density. The discrepancy of soil erosion area ratios is only 0.9% and 2.0%, respectively, under the 1% with 4% sampling density. Considering both field workload and prediction accuracy of soil erosion, the 1% sampling density with the grid calculation method is recommended when high resolution remotely sensed data are available. Otherwise, the 4% sampling density with either extrapolation method should be used. The research can provide useful reference information for ensuring the accuracy and reducing the survey workload on the application of CSLE in other areas. [ABSTRACT FROM AUTHOR]

Aims: Local activation time (LAT) mapping forms the cornerstone of atrial tachycardia diagnosis. Although anatomic and positional accuracy of electroanatomic mapping (EAM) systems have been validated, the effect of electrode sampling density on LAT map reconstruction is not known. Here, we study the effect of chamber geometry and activation complexity on optimal LAT sampling density using a combined in silico and in vivo approach.Methods and results: In vivo 21 atrial tachycardia maps were studied in three groups: (1) focal activation, (2) macro-re-entry, and (3) localized re-entry. In silico activation was simulated on a 4×4cm atrial monolayer, sampled randomly at 0.25-10 points/cm2 and used to re-interpolate LAT maps. Activation patterns were studied in the geometrically simple porcine right atrium (RA) and complex human left atrium (LA). Activation complexity was introduced into the porcine RA by incomplete inter-caval linear ablation. In all cases, optimal sampling density was defined as the highest density resulting in minimal further error reduction in the re-interpolated maps. Optimal sampling densities for LA tachycardias were 0.67 ± 0.17 points/cm2 (focal activation), 1.05 ± 0.32 points/cm2 (macro-re-entry) and 1.23 ± 0.26 points/cm2 (localized re-entry), P = 0.0031. Increasing activation complexity was associated with increased optimal sampling density both in silico (focal activation 1.09 ± 0.14 points/cm2; re-entry 1.44 ± 0.49 points/cm2; spiral-wave 1.50 ± 0.34 points/cm2, P < 0.0001) and in vivo (porcine RA pre-ablation 0.45 ± 0.13 vs. post-ablation 0.78 ± 0.17 points/cm2, P = 0.0008). Increasing chamber geometry was also associated with increased optimal sampling density (0.61 ± 0.22 points/cm2 vs. 1.0 ± 0.34 points/cm2, P = 0.0015).Conclusion: Optimal sampling densities can be identified to maximize diagnostic yield of LAT maps. Greater sampling density is required to correctly reveal complex activation and represent activation across complex geometries. Overall, the optimal sampling density for LAT map interpolation defined in this study was ∼1.0-1.5 points/cm2. [ABSTRACT FROM AUTHOR]

We provide sufficient density condition for a set of nonuniform samples to give rise to a set of sampling for multivariate bandlimited functions when the measurements consist of pointwise evaluations of a function and its first k derivatives. Along with explicit estimates of corresponding frame bounds, we derive the explicit density bound and show that, as k increases, it grows linearly in $$k+1$$ with the constant of proportionality $$1/\mathrm {e}$$ . Seeking larger gap conditions, we also prove a multivariate perturbation result for nonuniform samples that are sufficiently close to sets of sampling, e.g. to uniform samples taken at $$k+1$$ times the Nyquist rate. Additionally, in the univariate setting, we consider a related problem of so-called nonuniform bunched sampling, where in each sampling interval $$s+1$$ bunched measurements of a function are taken and the sampling intervals are permitted to be of different length. We derive an explicit density condition which grows linearly in $$s+1$$ for large s, with the constant of proportionality depending on the width of the bunches. The width of the bunches is allowed to be arbitrarily small, and moreover, for sufficiently narrow bunches and sufficiently large s, we obtain the same result as in the case of univariate sampling with s derivatives. [ABSTRACT FROM AUTHOR]

The knowledge on spatial distribution of soil properties by means of geostatistics is important as an indicator for a better soil use and management. This study aimed at evaluating the spatial distribution of soil chemical properties, in a forest and pasture area in Manicoré, Amazonas State, Brazil. Grids with 70.00 m x 70.00 m, with regular spacing of 10.00 m x 10.00 m, totaling 64 points, were established, and then soil samples were collected at the depths of 0.0-0.20 m and 0.40-0.60 m and had their chemical properties determined. Data were analyzed by using descriptive statistics and geostatistics, and the sampling density analysis was based on the coefficient of variation and semivariograms range. The mean and median values were adjusted to the closest values, indicating normal distribution, while the spherical, exponential and gaussian models were adjusted to the soil chemical properties. It was concluded that the geostatistics provided adequate information for understanding the spatial distribution. The forest area showed a higher spatial continuity and the pasture area a lower sampling density. The chemical properties showed differences in the spatial variability, while the range represented better the estimates for sampling density and spacing, in the forest and pasture area.

There is a great global demand for detailed soil property description; therefore, an ideal site-specific sampling has become indispensable to meet this demand. This study aimed to assess the implications of incorporating geological, geomorphological, and pedological information in reducing the required sampling density for magnetic susceptibility (MS), clay content (CC), and base saturation (BS) characterizations. The study area is located in Guatapará-SP (Brazil) and has 870 ha. A total of 371 samples were collected at a depth of 0–0.25 m for assessing magnetic susceptibility (MS), clay content, and base saturation (BS). A density of one sample was considered every 2.6, 3, 4, 5, 6, 7, 8, 9, 11, and 14 ha. The incorporation of secondary information in a geostatistical framework was performed by means of simple kriging with varying local means. Accuracy assessment of the spatial estimates at each sampling density, with and without incorporating secondary information, was performed by using external validation. For MS, geology and geomorphology information were responsible for about 45% and 44% spatial continuity, respectively. As for CC, these results were higher, being of 54% (geology) and 53% (geomorphology). Conversely, no spatial variability was detected for these properties by using pedological information. For BS, there was no relationship between secondary information and its spatial continuity. Incorporating geological and geomorphological information to MS data enabled a reduction in the number of samples required of 37% and 44%, respectively, in order to represent its spatial pattern. Likewise, this information provides a 35% reduction in the required sampling density for CC. However, secondary information was no helpful in decreasing sampling density for BS. In brief, incorporating pre-existing information can ensure a high quality of estimates and decrease the number of samples required for a detailed description for both MS and CC. Estimates of spatial patterns with geological and geomorphological information for modeling of soil properties might have a greater potential of use for environmental model composition. [ABSTRACT FROM AUTHOR]

Clay is an active component in the mechanical composition of soil. The quantitative study of the spatial distribution of soil clay content is crucial to soil microecological research and agricultural or environmental management. The main purpose of the paper was to simulate soil clay content in three dimensions and reveal its response to sampling density based on sequential Gaussian simulation. The results showed the following: (1) With a reduction in samples, especially in the A horizon, spatial correlation was relatively enhanced and randomness weakened. (2) The spatial distribution of soil clay showed soil had high clay content in the mid-eastern region of the Haidian District, Beijing, and clay content was generally low in the other areas; (3) With a decrease of sampling density, the simulated spatial distribution of clay became gradually more homogeneous. The stochastic simulation results for two kinds of sampling densities, i.e., SD 1 and SD 4 were closer to the original measured values; the general distribution was discrete and could more accurately reflect the local volatility of the original data distribution; (4) Considering the root mean square error (RMSE), accurate plot, standard error map, and quartile deviation map, SD 1 had the best effect of three-dimensional stochastic simulation, followed by SD 4 ; (5) For soil clay, three-dimensional sampling can be applicable to reduce samples required in the lower horizon in order to reduce the sampling workload. [ABSTRACT FROM AUTHOR]

Measuring natural ventilation rates in buildings with large vents with high accuracy and precision is not straight forward due to high spatial and temporal variabilities in the velocity distribution. Simplification of airflow rate measurements are mostly effectuated by lowering sampling density. Different sampling densities were investigated for both direct and tracer gas methods and compared with a detailed direct measurement method were in a naturally ventilated animal mock-up building. The results obtained by the reference method indicated that using only sampling locations in the middle of the side openings overestimated the airflow rate. In view of wind variations, better accuracy, precision and lower coefficients of variation were obtained with a higher number of sampling locations. The coefficients of variation varied between 5% for the reference and 29% using only one sampling location in the side outlet. In the ridge opening, only one middle sampling location was sufficient for an accuracy of 2% and a precision of 3%. The indirect tracer gas method gave varying concentrations with high confidence intervals resulting in non-significantly different measurement results between the different sampling strategies. The pattern of sampling locations was found to be very important resulting in different accuracies for a given sampling density. [ABSTRACT FROM AUTHOR]

Geographically weighted regression kriging (GWRK) is a popular interpolation method, considering not only spatial parametric non-stationarity and relationship between target and explanatory variables, but also spatial autocorrelation of residuals. However, little attention has been paid to the effects of different sampling densities on GWRK technique for estimating soil properties. Objectives of this study were: (i) comparing the GWRK predictions with those obtained from multiple linear regression kriging (MLRK) and ordinary kriging (OK), and (ii) examining how different sampling densities affect the performance of GWRK for predicting soil organic carbon (SOC). Soil samples were simulated with four sampling densities, including 0.010, 0.020, 0.041, and 0.082 sites/km 2 . The results showed that GWRK made less prediction errors and outperformed MLRK and OK in the case of a high sampling density, with the root mean squared errors of GWRKMLRK>OK. However, in the case of a low sampling density, GWRK generated larger prediction errors, exhibiting a poorer performance than MLRK and OK. Accordingly, we conclude that GWRK can be considered as the best approach for predicting SOC in these three approaches with sufficient data points, but it has a poorer performance than the other methods with sparse data points. [ABSTRACT FROM AUTHOR]

• Generalized linear geostatistical model (GLGM) was used for SOM mapping. • GLGM generally improved the accuracy of soil mapping. • Accuracy of GLGM is largely influenced by sampling densities and other factors. • More samples can make the best use of GLGM. The generalized linear geostatistical model (GLGM) is a formal approach of regression kriging that would be advantageous over commonly used modelling approaches for digital soil mapping (DSM). However, it has not been well explored in the literature, due to heavy computation. This study evaluates such formal approach for mapping soil organic matter at a regional scale (179,700 km2). We hypothesized that GLGM would be a better approach than other approaches as it can model nonlinear relationships and spatially consider the residuals. However, the accuracy would depend on sampling density. We compared GLGM with multiple linear regression (MLR), ordinary kriging (OK), regression kriging (RK), random forest (RF), generalized linear mixture model (GLMM), and generalized additive model (GAM). The effect of sampling density on the performance was also investigated by fitting the models based on resampling the samples with a series of sizes ranging from 100 to 1200. Results showed that GLGM generally improved the accuracy of DSM, compared with MLR, OK, RF, GLMM, and GAM, especially for large sample sizes, although the improvement was not significant. In a few cases, GLGM outperformed RK. The GLGM modelling and its prediction were largely influenced by sampling densities. Given small sample sizes, GLGM was unstable and the parameters were highly variable depending on the modelling realizations. Other influencing factors include linear and smooth correlations between soil and environmental covariates, spatial autocorrelation of the residuals, compatibility of spatial scales of soil samples and environmental covariates, and the scale of soil variation. For these factors, researchers with a more dense soil sampling are needed in future to explore the benefits of GLGM as a hybrid model for spatial prediction of soil. [ABSTRACT FROM AUTHOR]

The spatial variation of soil properties is a function of spatial scales. The spatial structure of soil properties in special scale can be discovered with single sample density, however, the whole variation could be discovered with multiple sample densities. The study area locates in Zhangjiagang County, which is a part of Yangtze River delta. Soil organic matter (SOM) is one of the important issues in global change research, thus in this paper the top SOM content in fertilized area is studied. Four hundred and thirty one samples were collected as the fundamental data, 11 levels of density or scale were designed with grid centroid sampling method, then classic statistics, semi-variance function, Moran's I, spatial fractal dimensions and Kriging interpolation were explored to investigate the variation of SOM in spatial structure, randomness and spatial patterns at different sampling densities. The results shows: There is a low influence of sampling density on SOM variation coefficient. The variation coefficients of SOM at 11 sampling densities are among 21.70%-23.12%, which belongs to the moderate variation. With the decrease of the sampling density or the increase of scale unit, the corresponding ratio of nugget/sill falls from 37.52% to 18.70%, fractal dimension falls from 1.838 to 1.714, and the Moran's I index increases from 0.485 to 0.857, which indicates the components of spatial structure variation is increasing and spatial correlation is gradually enhanced. The content of SOM shows decreasing trend from southwest to northeast. The spatial pattern of SOM content becomes simple with the decrease of sampling density since more and more details in micro-areas are ignored. In general, the model inference accuracy of SOM is reliable and the spatial character is stable when the sampling density is lower than 0.179 sample per km2. In consideration of science and economy, it can be concluded that the density of 0.179 sample per km2 can reflect the spatial randomness and structure of SOM in the study area, and this density can be regarded as the best sampling density. The paper takes sampling densities as cases to reveal the multiple spatial scale characteristics of SOM, and provides a helpful reference for hierarchical analysis of soil properties. [ABSTRACT FROM AUTHOR]