This paper extends the previously developed method of optimizing Road Weather Information Systems(RWIS)station placement by unveiling a sophisticated multi-variable semivariogram model that concurrently considers mult...This paper extends the previously developed method of optimizing Road Weather Information Systems(RWIS)station placement by unveiling a sophisticated multi-variable semivariogram model that concurrently considers multiple vital road weather variables.Previous research primarily centered on single-variable analysis focusing on road surface temperature(RST).The study bridges this oversight by introducing a framework that integrates multiple critical weather variables into the RWIS location allocation framework.This novel approach ensures balanced and equitable RWIS distribution across zones and aligns the network with areas both prone to traffic accidents and areas of high uncertainty.To demonstrate the effectiveness of this refinement,the authors applied the framework to Maine’s existing RWIS network,conducted a gap analysis through varying planning scenarios and generated optimal solutions using a heuristic optimization algorithm.The analysis identified areas that would benefit most from additional RWIS stations and guided optimal resource utilization across different road types and priority locations.A sensitivity analysis was also performed to evaluate the effect of different weightings for weather and traffic factors on the selection of optimal locations.The location solutions generated have been adopted by MaineDOT for future implementations,attesting to the model’s practicality and signifying an important advancement for more effective management of road weather conditions.展开更多
Semivariogram is applied to fracture data obtained from detailed scanline surveys of nine field sites in western New York, USA in order to investigate the spatial patterns of natural fractures. The length of the scanl...Semivariogram is applied to fracture data obtained from detailed scanline surveys of nine field sites in western New York, USA in order to investigate the spatial patterns of natural fractures. The length of the scanline is up to 36 m. How both fracture spacing and fracture length vary with distance is determined through semivariogram calculations. In this study, the authors developed a FORTRAN program to resample the fracture data from the scanline survey. By calculating experimental semivariogram, the authors found five different types of spatial patterns that can be described by linear, spherical, reversed spherical, polynomial I (for a<0) and polynomial II (for a>0) models, of which the last three are newly proposed in this study. The well-structured semivariograms of fracture spacing and length indicate that both the location of the fractures and the length distribution within their structure domains are not random. The results of this study also suggest that semivariograms can provide useful information in terms of spatial correlation distance for fracture location and fracture length. These semivariograms can also be utilized to design more efficient sampling schemes for further surveys, as well as to define the limits of highly probable extrapolation of a structure domain.展开更多
The spatial variation of soil nutrients in topsoil (0—20cm) was analyzed using semivariogram in the Zunhua County of Hebei Province, China. The effect on semivariogram with randomly deleted data and kriged estimates ...The spatial variation of soil nutrients in topsoil (0—20cm) was analyzed using semivariogram in the Zunhua County of Hebei Province, China. The effect on semivariogram with randomly deleted data and kriged estimates using various reduced sample sizes was also analyzed. The semivariograms of available N, total N, available P, organic matter were best described by a spherical model, except for available K, which best fitted a complex structure of exponential model and linear with sill model. The ratio of nugget to total sample variance ranged from 34.4% to 68.4%, indicating the spatial correlation of tested soil nutrients on a large scale was moderately dependent. Among five soil nutrients, available nitrogen and available phosphorus had the shortest spatial correlation range (5 km and 5.5 km), available K had the longest range (25.5 km), whereas total nitrogen and organic matter had intermediate spatial correlation range (14.5 km and 8.5 km). The semivariograms of available N, total N, available P, and organic matter were insensitive to a 50%—60% reduction in original sampling density, while for available K, it is up to 70%. The estimated spatial distributions of total N by kriging, under various reduced sample sizes, all correlated significantly ( P = 0.001) with those obtained from original data. The results showed that the semivariogram was a relatively robust tool when used in a large region and sufficient spatial variation information could be retained regardless of a higher deletion proportion of the original data. The original sample data could be reduced by kriging and the estimates showed no loss of spatial information, however, the results may be unreliable unless a clearly identified semivariogram model could be obtained. The results may provide useful information for determining the appropriate sampling densities for these scales of soil survey.展开更多
An exploratory spatial data analysis method(ESDA) was designed Apr.28,2002 for kriging monthly rainfall.Samples were monthly rainfall observed at 61 weather stations in eastern China over the period 1961-1998.Comparis...An exploratory spatial data analysis method(ESDA) was designed Apr.28,2002 for kriging monthly rainfall.Samples were monthly rainfall observed at 61 weather stations in eastern China over the period 1961-1998.Comparison of five semivariogram models(Spherical,Exponential,Linear,Gaussian and Rational Quadratic)indicated that kriging fulfills the objective of finding better ways to estimate interpolation weights and can provide error information for monthly rainfall interpolation.ESDA yielded the three most common forms of experimental semivariogram for monthly rainfall in the erea.All five models were appropriate for monthly rainflaa interpolation but under different circumstances.Spherical,Exponential and Linear models perform as smoothing interpolator of the data,whereas Gaussian and Rational Quadratic models serve as an exact interpolator.Spherical,Exponential and Linear models tend to underestimate the values,On the contrayr,Gaussian and Rational Quadratic models tend to overestimate the values.On the contrary,Gaussian and Rational Quadratic models tend to overestimate the values,Since the suitable model for a specific month usually is not unique and each model does not show any bias toward one or more specific months,an ESDA is recommended for a better interpolation result.展开更多
Inclusion of textures in image classification has been shown beneficial.This paper studies an efficient use of semivariogram features for object-based high-resolution image classification.First,an input image is divid...Inclusion of textures in image classification has been shown beneficial.This paper studies an efficient use of semivariogram features for object-based high-resolution image classification.First,an input image is divided into segments,for each of which a semivariogram is then calculated.Second,candidate features are extracted as a number of key locations of the semivariogram functions.Then we use an improved Relief algorithm and the principal component analysis to select independent and significant features.Then the selected prominent semivariogram features and the conventional spectral features are combined to constitute a feature vector for a support vector machine classifier.The effect of such selected semivariogram features is compared with those of the gray-level co-occurrence matrix(GLCM)features and window-based semivariogram texture features(STFs).Tests with aerial and satellite images show that such selected semivariogram features are of a more beneficial supplement to spectral features.The described method in this paper yields a higher classification accuracy than the combination of spectral and GLCM features or STFs.展开更多
基金supported by a grant from the MaineDOT and Vanasse Hangen Brustlin(VHB).Grant number:VHB 52874.03 WIN 026140.00,Name of the author who received the funding:Tae J.Kwon.
文摘This paper extends the previously developed method of optimizing Road Weather Information Systems(RWIS)station placement by unveiling a sophisticated multi-variable semivariogram model that concurrently considers multiple vital road weather variables.Previous research primarily centered on single-variable analysis focusing on road surface temperature(RST).The study bridges this oversight by introducing a framework that integrates multiple critical weather variables into the RWIS location allocation framework.This novel approach ensures balanced and equitable RWIS distribution across zones and aligns the network with areas both prone to traffic accidents and areas of high uncertainty.To demonstrate the effectiveness of this refinement,the authors applied the framework to Maine’s existing RWIS network,conducted a gap analysis through varying planning scenarios and generated optimal solutions using a heuristic optimization algorithm.The analysis identified areas that would benefit most from additional RWIS stations and guided optimal resource utilization across different road types and priority locations.A sensitivity analysis was also performed to evaluate the effect of different weightings for weather and traffic factors on the selection of optimal locations.The location solutions generated have been adopted by MaineDOT for future implementations,attesting to the model’s practicality and signifying an important advancement for more effective management of road weather conditions.
文摘Semivariogram is applied to fracture data obtained from detailed scanline surveys of nine field sites in western New York, USA in order to investigate the spatial patterns of natural fractures. The length of the scanline is up to 36 m. How both fracture spacing and fracture length vary with distance is determined through semivariogram calculations. In this study, the authors developed a FORTRAN program to resample the fracture data from the scanline survey. By calculating experimental semivariogram, the authors found five different types of spatial patterns that can be described by linear, spherical, reversed spherical, polynomial I (for a<0) and polynomial II (for a>0) models, of which the last three are newly proposed in this study. The well-structured semivariograms of fracture spacing and length indicate that both the location of the fractures and the length distribution within their structure domains are not random. The results of this study also suggest that semivariograms can provide useful information in terms of spatial correlation distance for fracture location and fracture length. These semivariograms can also be utilized to design more efficient sampling schemes for further surveys, as well as to define the limits of highly probable extrapolation of a structure domain.
基金TheNationalNaturalScienceFoundationofChina (No .498310 2 0 )
文摘The spatial variation of soil nutrients in topsoil (0—20cm) was analyzed using semivariogram in the Zunhua County of Hebei Province, China. The effect on semivariogram with randomly deleted data and kriged estimates using various reduced sample sizes was also analyzed. The semivariograms of available N, total N, available P, organic matter were best described by a spherical model, except for available K, which best fitted a complex structure of exponential model and linear with sill model. The ratio of nugget to total sample variance ranged from 34.4% to 68.4%, indicating the spatial correlation of tested soil nutrients on a large scale was moderately dependent. Among five soil nutrients, available nitrogen and available phosphorus had the shortest spatial correlation range (5 km and 5.5 km), available K had the longest range (25.5 km), whereas total nitrogen and organic matter had intermediate spatial correlation range (14.5 km and 8.5 km). The semivariograms of available N, total N, available P, and organic matter were insensitive to a 50%—60% reduction in original sampling density, while for available K, it is up to 70%. The estimated spatial distributions of total N by kriging, under various reduced sample sizes, all correlated significantly ( P = 0.001) with those obtained from original data. The results showed that the semivariogram was a relatively robust tool when used in a large region and sufficient spatial variation information could be retained regardless of a higher deletion proportion of the original data. The original sample data could be reduced by kriging and the estimates showed no loss of spatial information, however, the results may be unreliable unless a clearly identified semivariogram model could be obtained. The results may provide useful information for determining the appropriate sampling densities for these scales of soil survey.
文摘An exploratory spatial data analysis method(ESDA) was designed Apr.28,2002 for kriging monthly rainfall.Samples were monthly rainfall observed at 61 weather stations in eastern China over the period 1961-1998.Comparison of five semivariogram models(Spherical,Exponential,Linear,Gaussian and Rational Quadratic)indicated that kriging fulfills the objective of finding better ways to estimate interpolation weights and can provide error information for monthly rainfall interpolation.ESDA yielded the three most common forms of experimental semivariogram for monthly rainfall in the erea.All five models were appropriate for monthly rainflaa interpolation but under different circumstances.Spherical,Exponential and Linear models perform as smoothing interpolator of the data,whereas Gaussian and Rational Quadratic models serve as an exact interpolator.Spherical,Exponential and Linear models tend to underestimate the values,On the contrayr,Gaussian and Rational Quadratic models tend to overestimate the values.On the contrary,Gaussian and Rational Quadratic models tend to overestimate the values,Since the suitable model for a specific month usually is not unique and each model does not show any bias toward one or more specific months,an ESDA is recommended for a better interpolation result.
基金This work was supported by the National Natural Science Foundation of China[grant number 41101410]the Comprehensive Transportation Applications of High-resolution Remote Sensing program[grant number 07-Y30B10-9001-14/16]+1 种基金the Key Laboratory of Surveying Mapping and Geoinformation in Geographical Condition Monitoring[grant number 2014NGCM]the Science and Technology Plan of Sichuan Bureau of Surveying,Mapping and Geoinformation,China[grant number J2014ZC02].
文摘Inclusion of textures in image classification has been shown beneficial.This paper studies an efficient use of semivariogram features for object-based high-resolution image classification.First,an input image is divided into segments,for each of which a semivariogram is then calculated.Second,candidate features are extracted as a number of key locations of the semivariogram functions.Then we use an improved Relief algorithm and the principal component analysis to select independent and significant features.Then the selected prominent semivariogram features and the conventional spectral features are combined to constitute a feature vector for a support vector machine classifier.The effect of such selected semivariogram features is compared with those of the gray-level co-occurrence matrix(GLCM)features and window-based semivariogram texture features(STFs).Tests with aerial and satellite images show that such selected semivariogram features are of a more beneficial supplement to spectral features.The described method in this paper yields a higher classification accuracy than the combination of spectral and GLCM features or STFs.
