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
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. Comp...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 area. All five models were appropriate for monthly rainfall 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 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
Semivariogram is applied to fracture data obtained from detailed scanlinesurveys of nine field sites in western New York, USA in order to investigate the spatial patterns ofnatural fractures. The length of the scanlin...Semivariogram is applied to fracture data obtained from detailed scanlinesurveys of nine field sites in western New York, USA in order to investigate the spatial patterns ofnatural fractures. The length of the scanline is up to 36 m. How both fracture spacing and fracturelength vary with distance is determined through semivariogram calculations. In this study, theauthors developed a FORTRAN program to resample the fracture data from the scanline survey. Bycalculating experimental semivariogram, the authors found five different types of spatial patternsthat can be described by linear, spherical, reversed spherical, polynomial I (for aO) models, of which the last three arc newly proposed in this study. Thewell-structured semivariograms of fracture spacing and length indicate that both the location of thefractures and the length distribution within their structure domains are not random. The results ofthis study also suggest that semivariograms can provide useful information in terms of spatialcorrelation distance for fracture location and fracture length. These semivariograms can also beutilized to design more efficient sampling schemes for further surveys. as well as to define thelimits of highly probable extrapolation of a structure domain.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
Spatial heterogeneity is widely used in diverse applications, such as recognizing ecological process, guiding ecological restoration, managing land use, etc. Many researches have focused on the inherent scale multipli...Spatial heterogeneity is widely used in diverse applications, such as recognizing ecological process, guiding ecological restoration, managing land use, etc. Many researches have focused on the inherent scale multiplicity of spatial heterogeneity by using various environmental variables. How these variables affect their corresponding spatial heterogeneities, however, have received little attention. In this paper, we examined the effects of characteristics of normalized difference vegetation index （NDVI） and its related bands variable images, namely red and near infrared （NIR）, on their corresponding spatial heterogeneity detection based on variogram models. In a coastal wetland region, two groups of study sites with distinct fractal vegetation cover were tested and analyzed. The results show that： l） in high fractal vegetation cover （H-FVC） area, NDV! and NIR variables display a similar ability in detecting the spatial he- terogeneity caused by vegetation growing status structure; 2） in low fractal vegetation cover （L-FVC） area, the NIR and red variables outperform NDVI in the survey of soil spatial heterogeneity; and 3） generally, NIR variable is ubiquitously applicable for vegetation spatial heterogeneity investigation in different fractal vegetation covers. Moreover, as variable selection for remote sensing applications should fully take the characteristics of variables and the study object into account, the proposed variogram analysis method can make the variable selection objectively and scientifically, especially in studies related to spatial heterogeneity using remotely sensed data.展开更多
Spatial pattern and interdependence of different soil and plant parameters were examined in green bean field experiment carried out at the Mediterranean Agronomic Institute of Bari （MAIB）, Italy. The study aimed to ...Spatial pattern and interdependence of different soil and plant parameters were examined in green bean field experiment carried out at the Mediterranean Agronomic Institute of Bari （MAIB）, Italy. The study aimed to identify the spatial distribution of soil and plant parameters and their relationship at transects scale. The experiment consisted of three transects of 30 m length and 4.2 m width, irrigated with three different salinity levels （1 dSm＂1, 3 dSm1, 6 dSml）. Soil measurements （electrical conductivity and soil water content） were monitored along each transect in 24 sites, using TDR probe installed vertically at soil surface. Water storage was measured by using Diviner sensor for calculating directly the evapotranspiration fluxes along the whole soil profile under the different salinity levels imposed during the experiment. In the same 24 sites, crop monitoring involved measurements of Leaf Area Index （LAI）, Osmotic Potential （OP）, Root length Density （RID） and Evapotranspiration fluxes （ET）. Soil and plant properties were analyzed using both classical and geostatistical methods which included descriptive statistics, semivariograms and cross-semivariograms. Results indicated that moderate to large spatial variability existed across the field for soil and plant parameters, especially under the 6 dSm1 salinity treatment. A relatively satisfactory fit of the experimental cross-semivariogram was obtained for the 6 dS1, thus indicating similar spatial structures of the pairs of compared variables. By contrast, the experimental cross-semivariograms observed under the 3 dS~ treatment indicated no significant correlation structure between the compared variables. Overall, the results observed in the 3 dSm-1 were not significantly different from those obtained in the 1 dSm-1 transect and suggested a general insensitivity of the crop response to those levels of salinity.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 area. All five models were appropriate for monthly rainfall 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 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.
文摘Semivariogram is applied to fracture data obtained from detailed scanlinesurveys of nine field sites in western New York, USA in order to investigate the spatial patterns ofnatural fractures. The length of the scanline is up to 36 m. How both fracture spacing and fracturelength vary with distance is determined through semivariogram calculations. In this study, theauthors developed a FORTRAN program to resample the fracture data from the scanline survey. Bycalculating experimental semivariogram, the authors found five different types of spatial patternsthat can be described by linear, spherical, reversed spherical, polynomial I (for aO) models, of which the last three arc newly proposed in this study. Thewell-structured semivariograms of fracture spacing and length indicate that both the location of thefractures and the length distribution within their structure domains are not random. The results ofthis study also suggest that semivariograms can provide useful information in terms of spatialcorrelation distance for fracture location and fracture length. These semivariograms can also beutilized to design more efficient sampling schemes for further surveys. as well as to define thelimits of highly probable extrapolation of a structure domain.
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.
基金Under the auspices of National Key Technology Research and Development Program of China (No.2009BADB3B01-05)Knowledge Innovation Programs of Chinese Academy of Sciences (No. KSCX1-YW-09-13)
文摘Spatial heterogeneity is widely used in diverse applications, such as recognizing ecological process, guiding ecological restoration, managing land use, etc. Many researches have focused on the inherent scale multiplicity of spatial heterogeneity by using various environmental variables. How these variables affect their corresponding spatial heterogeneities, however, have received little attention. In this paper, we examined the effects of characteristics of normalized difference vegetation index （NDVI） and its related bands variable images, namely red and near infrared （NIR）, on their corresponding spatial heterogeneity detection based on variogram models. In a coastal wetland region, two groups of study sites with distinct fractal vegetation cover were tested and analyzed. The results show that： l） in high fractal vegetation cover （H-FVC） area, NDV! and NIR variables display a similar ability in detecting the spatial he- terogeneity caused by vegetation growing status structure; 2） in low fractal vegetation cover （L-FVC） area, the NIR and red variables outperform NDVI in the survey of soil spatial heterogeneity; and 3） generally, NIR variable is ubiquitously applicable for vegetation spatial heterogeneity investigation in different fractal vegetation covers. Moreover, as variable selection for remote sensing applications should fully take the characteristics of variables and the study object into account, the proposed variogram analysis method can make the variable selection objectively and scientifically, especially in studies related to spatial heterogeneity using remotely sensed data.
文摘Spatial pattern and interdependence of different soil and plant parameters were examined in green bean field experiment carried out at the Mediterranean Agronomic Institute of Bari （MAIB）, Italy. The study aimed to identify the spatial distribution of soil and plant parameters and their relationship at transects scale. The experiment consisted of three transects of 30 m length and 4.2 m width, irrigated with three different salinity levels （1 dSm＂1, 3 dSm1, 6 dSml）. Soil measurements （electrical conductivity and soil water content） were monitored along each transect in 24 sites, using TDR probe installed vertically at soil surface. Water storage was measured by using Diviner sensor for calculating directly the evapotranspiration fluxes along the whole soil profile under the different salinity levels imposed during the experiment. In the same 24 sites, crop monitoring involved measurements of Leaf Area Index （LAI）, Osmotic Potential （OP）, Root length Density （RID） and Evapotranspiration fluxes （ET）. Soil and plant properties were analyzed using both classical and geostatistical methods which included descriptive statistics, semivariograms and cross-semivariograms. Results indicated that moderate to large spatial variability existed across the field for soil and plant parameters, especially under the 6 dSm1 salinity treatment. A relatively satisfactory fit of the experimental cross-semivariogram was obtained for the 6 dS1, thus indicating similar spatial structures of the pairs of compared variables. By contrast, the experimental cross-semivariograms observed under the 3 dS~ treatment indicated no significant correlation structure between the compared variables. Overall, the results observed in the 3 dSm-1 were not significantly different from those obtained in the 1 dSm-1 transect and suggested a general insensitivity of the crop response to those levels of salinity.
