The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform
Alan B Anderson - One of the best experts on this subject based on the ideXlab platform.
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Modeling and Prediction of Land Condition for Fort Riley Military Installation
Transactions of the ASABE, 2013Co-Authors: Heidi R. Howard, Guangxing Wang, Steve Singer, Alan B AndersonAbstract:Abstract. In the U.S., the Department of Defense manages more than 5500 military installations that occupy approximately 12 million ha of land. These lands are used for various military training programs. Training activities inevitably degrade the land condition, and the degraded land condition, in turn, limits the land’s military training carrying capacity. To sustain the military training land carrying capacity and the environment, land managers must monitor and predict changes to the land condition under various military training schemes. The objective of this study is to develop prediction models for land condition based on military training intensity and on independent variables that play a significant role in driving land condition changes at Fort Riley, Kansas. It is assumed that land condition can be quantified using soil erosion as a surrogate measure, which is mainly determined by a ground and vegetation Cover Factor, in which the larger the Factor, the poorer the land condition. In addition to military training intensity, the independent variables used in these prediction models of land condition included distance from the location to roads, terrain slope (which affects military training access), ground Cover, landscape fragmentation (an indirect measure of military training induced disturbance), and spatial variability of canopy Cover and military training induced disturbance (as reflected in Landsat Thematic Mapper [TM] images). Various regression models were developed, and predictions made by linear and nonlinear models were compared with and without TM images, with and without stepwise regression procedures, and with and without historical land condition variables. Results showed that the absolute Pearson product moment correlation coefficients of ground Cover with the Cover Factor were larger than 0.63; the correlation was greatest and significant at a risk level of 5%. Ground Cover was thus involved in all the stepwise regression and nonlinear models. Although military training intensity was significantly correlated with the Cover Factor, training intensity was excluded from the best models mainly because both ground Cover and landscape fragmentation that existed in the models also reflected the military training induced disturbance. Compared to models in which all the variables were involved, the stepwise regression models reduced the number of the independent variables from 11 or 15 to 3 or 6 (depending on analysis year) with no significant loss of accuracy. In most cases, adding the near and middle infrared TM images, which revealed the spatial variability of military training induced disturbance, improved the prediction of land condition. Based on the correlation coefficient and root mean square error (RMSE) between the predicted and observed values of the Cover Factor, the nonlinear models that used significant independent variables led to more accurate predictions than the linear regression models. This suggests that the combination of stepwise regression and nonlinear models could increase the accuracy of prediction. Moreover, adding the historical land condition variables, such as historical Cover Factor and ground Cover, into the models could greatly decrease prediction errors.
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efficiencies of remotely sensed data and sensitivity of grid spacing in sampling and mapping a soil erosion relevant Cover Factor by cokriging
International Journal of Remote Sensing, 2009Co-Authors: Guangxing Wang, George Z Gertner, Alan B AndersonAbstract:This study investigates applications and efficiencies of remotely sensed data and the sensitivity of grid spacing for the sampling and mapping of a ground and vegetation Cover Factor in a monitoring system of soil erosion dynamics by cokriging with Landsat Thematic Mapper (TM) imagery based on regionalized variable theory. The results show that using image data can greatly reduce the number of ground sample plots and sampling cost required for collection of data. Under the same precision requirement, the efficiency gain is significant as the ratio of ground to image data used varies from 1: 1 to 1: 16. Moreover, we proposed and discussed several modifications to the cokriging procedure with image data for sampling and mapping. First, directly using neighbouring pixels for image data in sampling design and mapping is more efficient at increasing the accuracy of maps than using sampled pixels. Although information among neighbouring pixels might be considered redundant, spatial cross-correlation of spectral...
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optimal spatial resolution for collection of ground data and multi sensor image mapping of a soil erosion Cover Factor
Journal of Environmental Management, 2008Co-Authors: Guangxing Wang, George Z Gertner, Heidi R. Howard, Alan B AndersonAbstract:Abstract Military training activities disturb ground and vegetation Cover of landscapes and increases potential soil erosion. To monitor the dynamics of soil erosion, there is an important need for an optimal sampling design in which determining the optimal spatial resolutions in terms of size of sample plots used for the collection of ground data and the size of pixels for mapping. Given a sample size, an optimal spatial resolution should be cost-efficient in both sampling costs and map accuracy. This study presents a spatial variability-based method for that purpose and compared it with the traditional methods in a study area in which a soil erosion Cover Factor was sampled and mapped with multiple plot sizes and multi-sensor images. The results showed that the optimal spatial resolutions obtained using the spatial variability-based method were 12 and 20 m for years 1999 and 2000, respectively, and were consistent with those using the traditional methods. Moreover, the most appropriate spatial resolutions using the high-resolution images were also consistent with those using ground sample data, which provides a potential to use the high-resolution images instead of ground data to determine the optimal spatial resolutions before sampling. The most appropriate spatial resolutions above were then verified in terms of cost-efficiency which was defined as the product of sampling cost and map error using ordinary kriging without images and sequential Gaussian co-simulation with images to generate maps.
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combining stratification and up scaling method block cokriging with remote sensing imagery for sampling and mapping an erosion Cover Factor
Ecological Informatics, 2007Co-Authors: George Z Gertner, Guangxing Wang, Alan B Anderson, Heidi R. HowardAbstract:Abstract When a ground and vegetation Cover Factor related to soil erosion is mapped with the aid of remotely sensed data, a cost-efficient sample design to collect ground data and to obtain an accurate map is required. However, the supports used to collect ground data are often smaller than the desirable pixels used for mapping, which leads to complexity in developing procedures for sample design and mapping. For these purposes, a sampling and mapping method was developed by integrating stratification and an up-scaling method in geostatistics — block cokriging with Landsat Thematic Mapper imagery. This method is based on spatial correlation and stratified sampling. It scales up not only the ground sample data but also the uncertainties associated with the data aggregation from smaller supports to larger pixels or blocks. This method uses the advantages of both stratification and block cokriging variance-based sample design, which leads to sample designs with variable grid spacing, and thus significantly increases the unit cost-efficiency of sample data in sampling and mapping. This outcome was verified by the results of this study.
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sampling and mapping a soil erosion Cover Factor by integrating stratification model updating and cokriging with images
Environmental Management, 2007Co-Authors: Guangxing Wang, George Z Gertner, Alan B AndersonAbstract:Cost-efficient sample designs for collection of ground data and accurate mapping of variables are required to monitor natural resources and environmental and ecological systems. In this study, a sample design and mapping method was developed by integrating stratification, model updating, and cokriging with Landsat Thematic Mapper (TM) imagery. This method is based on the spatial autocorrelation of variables and the spatial cross-correlation among them. It can lead to sample designs with variable grid spacing, where sampling distances between plots vary depending on spatial variability of the variables from location to location. This has potential cost-efficiencies in terms of sample design and mapping. This method is also applicable for mapping in the case in which no ground data can be collected in some parts of a study area because of the high cost. The method was validated in a case study in which a ground and vegetation Cover Factor was sampled and mapped for monitoring soil erosion. The results showed that when the sample obtained with three strata using the developed method was used for sampling and mapping the Cover Factor, the sampling cost was greatly decreased, although the error of the map was slightly increased compared to that without stratification; that is, the sample cost-efficiency quantified by the product of cost and error was greatly increased. The increase of cost-efficiency was more obvious when the Cover Factor values of the plots within the no-significant-change stratum were updated by a model developed using the previous observations instead of remeasuring them in the field.
George Z Gertner - One of the best experts on this subject based on the ideXlab platform.
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efficiencies of remotely sensed data and sensitivity of grid spacing in sampling and mapping a soil erosion relevant Cover Factor by cokriging
International Journal of Remote Sensing, 2009Co-Authors: Guangxing Wang, George Z Gertner, Alan B AndersonAbstract:This study investigates applications and efficiencies of remotely sensed data and the sensitivity of grid spacing for the sampling and mapping of a ground and vegetation Cover Factor in a monitoring system of soil erosion dynamics by cokriging with Landsat Thematic Mapper (TM) imagery based on regionalized variable theory. The results show that using image data can greatly reduce the number of ground sample plots and sampling cost required for collection of data. Under the same precision requirement, the efficiency gain is significant as the ratio of ground to image data used varies from 1: 1 to 1: 16. Moreover, we proposed and discussed several modifications to the cokriging procedure with image data for sampling and mapping. First, directly using neighbouring pixels for image data in sampling design and mapping is more efficient at increasing the accuracy of maps than using sampled pixels. Although information among neighbouring pixels might be considered redundant, spatial cross-correlation of spectral...
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optimal spatial resolution for collection of ground data and multi sensor image mapping of a soil erosion Cover Factor
Journal of Environmental Management, 2008Co-Authors: Guangxing Wang, George Z Gertner, Heidi R. Howard, Alan B AndersonAbstract:Abstract Military training activities disturb ground and vegetation Cover of landscapes and increases potential soil erosion. To monitor the dynamics of soil erosion, there is an important need for an optimal sampling design in which determining the optimal spatial resolutions in terms of size of sample plots used for the collection of ground data and the size of pixels for mapping. Given a sample size, an optimal spatial resolution should be cost-efficient in both sampling costs and map accuracy. This study presents a spatial variability-based method for that purpose and compared it with the traditional methods in a study area in which a soil erosion Cover Factor was sampled and mapped with multiple plot sizes and multi-sensor images. The results showed that the optimal spatial resolutions obtained using the spatial variability-based method were 12 and 20 m for years 1999 and 2000, respectively, and were consistent with those using the traditional methods. Moreover, the most appropriate spatial resolutions using the high-resolution images were also consistent with those using ground sample data, which provides a potential to use the high-resolution images instead of ground data to determine the optimal spatial resolutions before sampling. The most appropriate spatial resolutions above were then verified in terms of cost-efficiency which was defined as the product of sampling cost and map error using ordinary kriging without images and sequential Gaussian co-simulation with images to generate maps.
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combining stratification and up scaling method block cokriging with remote sensing imagery for sampling and mapping an erosion Cover Factor
Ecological Informatics, 2007Co-Authors: George Z Gertner, Guangxing Wang, Alan B Anderson, Heidi R. HowardAbstract:Abstract When a ground and vegetation Cover Factor related to soil erosion is mapped with the aid of remotely sensed data, a cost-efficient sample design to collect ground data and to obtain an accurate map is required. However, the supports used to collect ground data are often smaller than the desirable pixels used for mapping, which leads to complexity in developing procedures for sample design and mapping. For these purposes, a sampling and mapping method was developed by integrating stratification and an up-scaling method in geostatistics — block cokriging with Landsat Thematic Mapper imagery. This method is based on spatial correlation and stratified sampling. It scales up not only the ground sample data but also the uncertainties associated with the data aggregation from smaller supports to larger pixels or blocks. This method uses the advantages of both stratification and block cokriging variance-based sample design, which leads to sample designs with variable grid spacing, and thus significantly increases the unit cost-efficiency of sample data in sampling and mapping. This outcome was verified by the results of this study.
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sampling and mapping a soil erosion Cover Factor by integrating stratification model updating and cokriging with images
Environmental Management, 2007Co-Authors: Guangxing Wang, George Z Gertner, Alan B AndersonAbstract:Cost-efficient sample designs for collection of ground data and accurate mapping of variables are required to monitor natural resources and environmental and ecological systems. In this study, a sample design and mapping method was developed by integrating stratification, model updating, and cokriging with Landsat Thematic Mapper (TM) imagery. This method is based on the spatial autocorrelation of variables and the spatial cross-correlation among them. It can lead to sample designs with variable grid spacing, where sampling distances between plots vary depending on spatial variability of the variables from location to location. This has potential cost-efficiencies in terms of sample design and mapping. This method is also applicable for mapping in the case in which no ground data can be collected in some parts of a study area because of the high cost. The method was validated in a case study in which a ground and vegetation Cover Factor was sampled and mapped for monitoring soil erosion. The results showed that when the sample obtained with three strata using the developed method was used for sampling and mapping the Cover Factor, the sampling cost was greatly decreased, although the error of the map was slightly increased compared to that without stratification; that is, the sample cost-efficiency quantified by the product of cost and error was greatly increased. The increase of cost-efficiency was more obvious when the Cover Factor values of the plots within the no-significant-change stratum were updated by a model developed using the previous observations instead of remeasuring them in the field.
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sampling designs over time based on spatial variability of images for mapping and monitoring soil erosion Cover Factor
Annual Conference of the American Society for Photogrammetry and Remote Sensing 2006: Prospecting for Geospatial Information Integration ASPRS 2006, 2006Co-Authors: Guangxing Wang, Alan B Anderson, George Z GertnerAbstract:In the Revised Universal Soil Loss Equation, Cover Factor reflects the effect of ground and vegetation Covers on the reduction of soil loss and it controls change of soil erosion for a specific area. Developing optimal sampling designs over time for data collection of this Factor is thus critical to monitor the dynamics of soil erosion. In this study we developed an image–inferred semivariogram based method to determine optimal sample size. We further explored spatial and temporal variability, and the change of sample sizes needed over time for this Cover Factor. In addition, we studied application of historical ground data and uncertainties to infer semivariograms by combining the Landsat thematical mapper (TM) images to determine sample sizes. Compared to the results using ground data, the semivariogram and its dynamics of the Cover Factor could be successfully inferred using the multi-temporal TM images. The accuracy of sample sizes obtained using the image-inferred semivariograms could meet the requirement for regional estimation, but for local estimation for mapping it was very much dependent on the quality and correlation of the images with the Factor. Moreover, historical ground data should be used with great caution for sampling design.
Guangxing Wang - One of the best experts on this subject based on the ideXlab platform.
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comparison of military and nonmilitary land condition using an image derived soil erosion Cover Factor
Journal of Soil and Water Conservation, 2017Co-Authors: Santosh Rijal, Guangxing Wang, Heidi R. Howard, Philip B Woodford, Justin T Schoof, Tonny J Oyana, L O Park, R LiAbstract:Land condition of military installations varies spatially and temporally mainly due to variable intensity and frequency of military training induced disturbance. The existing ground-based methods to assess the land condition are costly and time consuming. There is thus a strong need to develop an efficient and low-cost methodology for modeling and monitoring of the land condition for military installations and compare it with nonmilitary land to assess the training induced impacts. For this purpose, in this study Fort Riley military installation (FR) and its neighboring Konza Prairie Biological Station (KPBS) were chosen. A remote sensing based linear spectral mixture (LSM) analysis method was proposed to model, monitor, and compare the land conditions of both sites by developing a soil erosion relevant and image derived Cover Factor (ICF) using both Landsat Thematic Mapper and Moderate Resolution Imaging Spectroradiometer (MODIS) images. The results showed that (1) the LSM based ICF was statistically significantly correlated with military training induced disturbance intensity and can be used to quantify the land conditions; (2) the ICF provided the potential to generate spatially and temporally explicit estimates of the land conditions; (3) the ICF time series revealed that the land condition of FR fluctuated with a slight decreasing trend of ICF values, but statistically the trend was not significant; and (4) small ICF values dominated both FR and KPBS, but compared to that in the nonmilitary land KPBS, military training led to significantly poorer land condition in the military land FR. Overall, the ICF method provides a potential tool to spatially and temporally assess land conditions of military installations. It is expected that this method can be applied to assess land conditions for other military and nonmilitary areas.
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Modeling and Prediction of Land Condition for Fort Riley Military Installation
Transactions of the ASABE, 2013Co-Authors: Heidi R. Howard, Guangxing Wang, Steve Singer, Alan B AndersonAbstract:Abstract. In the U.S., the Department of Defense manages more than 5500 military installations that occupy approximately 12 million ha of land. These lands are used for various military training programs. Training activities inevitably degrade the land condition, and the degraded land condition, in turn, limits the land’s military training carrying capacity. To sustain the military training land carrying capacity and the environment, land managers must monitor and predict changes to the land condition under various military training schemes. The objective of this study is to develop prediction models for land condition based on military training intensity and on independent variables that play a significant role in driving land condition changes at Fort Riley, Kansas. It is assumed that land condition can be quantified using soil erosion as a surrogate measure, which is mainly determined by a ground and vegetation Cover Factor, in which the larger the Factor, the poorer the land condition. In addition to military training intensity, the independent variables used in these prediction models of land condition included distance from the location to roads, terrain slope (which affects military training access), ground Cover, landscape fragmentation (an indirect measure of military training induced disturbance), and spatial variability of canopy Cover and military training induced disturbance (as reflected in Landsat Thematic Mapper [TM] images). Various regression models were developed, and predictions made by linear and nonlinear models were compared with and without TM images, with and without stepwise regression procedures, and with and without historical land condition variables. Results showed that the absolute Pearson product moment correlation coefficients of ground Cover with the Cover Factor were larger than 0.63; the correlation was greatest and significant at a risk level of 5%. Ground Cover was thus involved in all the stepwise regression and nonlinear models. Although military training intensity was significantly correlated with the Cover Factor, training intensity was excluded from the best models mainly because both ground Cover and landscape fragmentation that existed in the models also reflected the military training induced disturbance. Compared to models in which all the variables were involved, the stepwise regression models reduced the number of the independent variables from 11 or 15 to 3 or 6 (depending on analysis year) with no significant loss of accuracy. In most cases, adding the near and middle infrared TM images, which revealed the spatial variability of military training induced disturbance, improved the prediction of land condition. Based on the correlation coefficient and root mean square error (RMSE) between the predicted and observed values of the Cover Factor, the nonlinear models that used significant independent variables led to more accurate predictions than the linear regression models. This suggests that the combination of stepwise regression and nonlinear models could increase the accuracy of prediction. Moreover, adding the historical land condition variables, such as historical Cover Factor and ground Cover, into the models could greatly decrease prediction errors.
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efficiencies of remotely sensed data and sensitivity of grid spacing in sampling and mapping a soil erosion relevant Cover Factor by cokriging
International Journal of Remote Sensing, 2009Co-Authors: Guangxing Wang, George Z Gertner, Alan B AndersonAbstract:This study investigates applications and efficiencies of remotely sensed data and the sensitivity of grid spacing for the sampling and mapping of a ground and vegetation Cover Factor in a monitoring system of soil erosion dynamics by cokriging with Landsat Thematic Mapper (TM) imagery based on regionalized variable theory. The results show that using image data can greatly reduce the number of ground sample plots and sampling cost required for collection of data. Under the same precision requirement, the efficiency gain is significant as the ratio of ground to image data used varies from 1: 1 to 1: 16. Moreover, we proposed and discussed several modifications to the cokriging procedure with image data for sampling and mapping. First, directly using neighbouring pixels for image data in sampling design and mapping is more efficient at increasing the accuracy of maps than using sampled pixels. Although information among neighbouring pixels might be considered redundant, spatial cross-correlation of spectral...
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optimal spatial resolution for collection of ground data and multi sensor image mapping of a soil erosion Cover Factor
Journal of Environmental Management, 2008Co-Authors: Guangxing Wang, George Z Gertner, Heidi R. Howard, Alan B AndersonAbstract:Abstract Military training activities disturb ground and vegetation Cover of landscapes and increases potential soil erosion. To monitor the dynamics of soil erosion, there is an important need for an optimal sampling design in which determining the optimal spatial resolutions in terms of size of sample plots used for the collection of ground data and the size of pixels for mapping. Given a sample size, an optimal spatial resolution should be cost-efficient in both sampling costs and map accuracy. This study presents a spatial variability-based method for that purpose and compared it with the traditional methods in a study area in which a soil erosion Cover Factor was sampled and mapped with multiple plot sizes and multi-sensor images. The results showed that the optimal spatial resolutions obtained using the spatial variability-based method were 12 and 20 m for years 1999 and 2000, respectively, and were consistent with those using the traditional methods. Moreover, the most appropriate spatial resolutions using the high-resolution images were also consistent with those using ground sample data, which provides a potential to use the high-resolution images instead of ground data to determine the optimal spatial resolutions before sampling. The most appropriate spatial resolutions above were then verified in terms of cost-efficiency which was defined as the product of sampling cost and map error using ordinary kriging without images and sequential Gaussian co-simulation with images to generate maps.
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combining stratification and up scaling method block cokriging with remote sensing imagery for sampling and mapping an erosion Cover Factor
Ecological Informatics, 2007Co-Authors: George Z Gertner, Guangxing Wang, Alan B Anderson, Heidi R. HowardAbstract:Abstract When a ground and vegetation Cover Factor related to soil erosion is mapped with the aid of remotely sensed data, a cost-efficient sample design to collect ground data and to obtain an accurate map is required. However, the supports used to collect ground data are often smaller than the desirable pixels used for mapping, which leads to complexity in developing procedures for sample design and mapping. For these purposes, a sampling and mapping method was developed by integrating stratification and an up-scaling method in geostatistics — block cokriging with Landsat Thematic Mapper imagery. This method is based on spatial correlation and stratified sampling. It scales up not only the ground sample data but also the uncertainties associated with the data aggregation from smaller supports to larger pixels or blocks. This method uses the advantages of both stratification and block cokriging variance-based sample design, which leads to sample designs with variable grid spacing, and thus significantly increases the unit cost-efficiency of sample data in sampling and mapping. This outcome was verified by the results of this study.
Halil Rifat Alpay - One of the best experts on this subject based on the ideXlab platform.
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Assessing the colour changes of woven polyester automotive seat Cover fabrics under ultraviolet light
International Journal of Vehicle Design, 2017Co-Authors: Mine Akgun, Behcet Becerir, Halil Rifat AlpayAbstract:This paper assesses the changes in colour differences (ΔE*ab), lightness (L*), chroma (C*) and colour strength (K/S) values of polyester automotive seat Cover fabrics after five durations of exposure to ultraviolet (UV) light using different fabric constructional parameters. At each level, the UV-exposed fabric was compared with the fabric in its original state and the colour properties were determined. The ΔE*ab colour difference values of all of the fabrics increased as the fabric Cover Factors decreased. The chroma and colour strength values decreased and the lightness values increased as the exposure times increased. Compact fabric structures with high Cover Factor values that were woven from high-count yarns with a high number of yarn crossings were found to be more resistant to UV exposure. To produce fabrics that are colourfast in UV light, the constructional parameters of the fabrics must be carefully chosen in terms of yarn and fabric constructional properties.
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Usage of proportions method for predicting percentage reflectance of woven structures in fabric design
2015Co-Authors: Mine Akgun, Behcet Becerir, Halil Rifat AlpayAbstract:Effects of fabric constructional parameters on the prediction of percentage reflectance of woven polyester fabrics for different weft yarn densities have been investigated considering the same yarn count and weave pattern. Relationship among weft yarn density, fabric bulk density, fabric Cover Factor and percentage reflectance has been studied using the proportions method. The relationship between measured and proportionally predicted percentage reflectance values shows that proportional prediction method, according to fabric Cover Factors and using the same yarn count and the weave pattern but different yarn densities, gives the closest results to the measured ones. This could be used in estimation of fabric percentage reflectance for fabric design. Fabrics with different constructional parameters but having the same percentage reflectance could be produced by using proportions method under the conditions when appropriate beginning parameters are selected along with the fabric Cover Factors.
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Reflectance prediction of colored polyester fabrics by a novel formula
Fibers and Polymers, 2014Co-Authors: Mine Akgun, Behcet Becerir, Halil Rifat AlpayAbstract:This paper focuses on the reflectance prediction of colored (unicolored) fabrics considering relationship between fractional reflectance values and Cover Factors of fabrics woven from polyester yarns. A novel equation for the calculation of relation between fractional reflectance and Cover Factor was proposed and usage of the equation was assessed by reflectance measurements. 48 dyed polyester fabrics having different constructional parameters were used and fabrics differed from each other by their Cover Factors. Warp yarn type and count, warp density and warp yarn twist were the same but weft yarn count, weft yarn fiber count and weft density were different for the fabrics in experimental sub-groups. The reflectance measurements were conducted on the dyed fabric samples as well as on the individual yarn systems (warp and weft) of the same fabrics. The proposed equation was tested according to different fabric constructional parameters and reasonable results with the experimental data were obtained. The possibilities of general use of derived mathematical relations between theoretical and measured reflectance values were researched. The relation obtained was used to explain the effects of different constructional parameters on reflectance behavior of fabric surfaces.
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Assessing the relationship among fabric constructional parameters, fractional reflectances and Cover Factors of polyester fabrics by experimental and mathematical methods
Fibers and Polymers, 2010Co-Authors: Mine Akgun, Behcet Becerir, Halil Rifat AlpayAbstract:This paper focuses on the assessment of the relation among constructional properties, fractional reflectances and Cover Factors of fabrics woven from polyester yarns. A novel equation for the calculation of the relation between fractional reflectance and fabric Cover Factor was proposed and the usage of the equation was assessed by reflectance measurements. 48 polyester fabrics having different constructional parameters were used and the fabrics differed from each other by their Cover Factors. The warp yarn type and count, warp density and warp yarn twist were the same but weft yarn count, weft yarn fiber count and weft density were different for the fabrics in the experimental sub-groups. The reflectance measurements were conducted on the pretreated but undyed fabric samples as well as on the individual yarn systems of the same fabrics. Fabrics with the same Cover Factors exhibited different fractional reflectances. Reflectances were found to be dependent on the Cover Factor as well as on yarn fiber fineness, yarn count, yarn density and fabric weave. The changes in crimp of the yarns according to different construction parameters also governed the changes in fractional reflectances of fabric surfaces. The proposed equation was tested according to different fabric construction parameters and it was concluded that fiber fineness and weave pattern were among the most important parameters which govern the total light reflectances from the fabric surfaces, although they are not incorporated in the calculation of the fabric Cover Factors. The proposed equation was used to explain the effects of these components on the reflectance behavior of the fabric surfaces and on fabric Cover.
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Assessment of Color Strength and Chroma Values of Polyester Fabrics having Different Cover Factors after Abrasion
Textile Research Journal, 2008Co-Authors: Mine Akgun, Behcet Becerir, Halil Rifat AlpayAbstract:This paper focuses on the assessment of color strength and chroma values that occur in dyed polyester fabrics after different cycles of abrasion for different fabric construction parameters. We used 12 polyester fabrics with various construction parameters in two experimental sets. The fabrics differed from each other by their Cover Factors. The warp yarn type and count, warp density, warp yarn twist and the fabric weave were the same for all of the fabrics in each experimental set. The dyeing of the fabric samples was performed by using a commercial disperse dye (CI Disperse Red 74:1). Four different abrasion cycles (2500, 5000, 7500, 10,000) were used. The main differences in color strength and chroma values were observed between 0 and 2500 cycles of abrasion. Increasing abrasion cycles after 2500 changed the appearance of the fabrics woven from staple weft yarns considerably. It is concluded that the rubbing motion of the Martindale instrument is related to the Cover Factor of the fabrics and dyeing co...
Mine Akgun - One of the best experts on this subject based on the ideXlab platform.
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Assessing the colour changes of woven polyester automotive seat Cover fabrics under ultraviolet light
International Journal of Vehicle Design, 2017Co-Authors: Mine Akgun, Behcet Becerir, Halil Rifat AlpayAbstract:This paper assesses the changes in colour differences (ΔE*ab), lightness (L*), chroma (C*) and colour strength (K/S) values of polyester automotive seat Cover fabrics after five durations of exposure to ultraviolet (UV) light using different fabric constructional parameters. At each level, the UV-exposed fabric was compared with the fabric in its original state and the colour properties were determined. The ΔE*ab colour difference values of all of the fabrics increased as the fabric Cover Factors decreased. The chroma and colour strength values decreased and the lightness values increased as the exposure times increased. Compact fabric structures with high Cover Factor values that were woven from high-count yarns with a high number of yarn crossings were found to be more resistant to UV exposure. To produce fabrics that are colourfast in UV light, the constructional parameters of the fabrics must be carefully chosen in terms of yarn and fabric constructional properties.
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Usage of proportions method for predicting percentage reflectance of woven structures in fabric design
2015Co-Authors: Mine Akgun, Behcet Becerir, Halil Rifat AlpayAbstract:Effects of fabric constructional parameters on the prediction of percentage reflectance of woven polyester fabrics for different weft yarn densities have been investigated considering the same yarn count and weave pattern. Relationship among weft yarn density, fabric bulk density, fabric Cover Factor and percentage reflectance has been studied using the proportions method. The relationship between measured and proportionally predicted percentage reflectance values shows that proportional prediction method, according to fabric Cover Factors and using the same yarn count and the weave pattern but different yarn densities, gives the closest results to the measured ones. This could be used in estimation of fabric percentage reflectance for fabric design. Fabrics with different constructional parameters but having the same percentage reflectance could be produced by using proportions method under the conditions when appropriate beginning parameters are selected along with the fabric Cover Factors.
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Reflectance prediction of colored polyester fabrics by a novel formula
Fibers and Polymers, 2014Co-Authors: Mine Akgun, Behcet Becerir, Halil Rifat AlpayAbstract:This paper focuses on the reflectance prediction of colored (unicolored) fabrics considering relationship between fractional reflectance values and Cover Factors of fabrics woven from polyester yarns. A novel equation for the calculation of relation between fractional reflectance and Cover Factor was proposed and usage of the equation was assessed by reflectance measurements. 48 dyed polyester fabrics having different constructional parameters were used and fabrics differed from each other by their Cover Factors. Warp yarn type and count, warp density and warp yarn twist were the same but weft yarn count, weft yarn fiber count and weft density were different for the fabrics in experimental sub-groups. The reflectance measurements were conducted on the dyed fabric samples as well as on the individual yarn systems (warp and weft) of the same fabrics. The proposed equation was tested according to different fabric constructional parameters and reasonable results with the experimental data were obtained. The possibilities of general use of derived mathematical relations between theoretical and measured reflectance values were researched. The relation obtained was used to explain the effects of different constructional parameters on reflectance behavior of fabric surfaces.
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Assessing the relationship among fabric constructional parameters, fractional reflectances and Cover Factors of polyester fabrics by experimental and mathematical methods
Fibers and Polymers, 2010Co-Authors: Mine Akgun, Behcet Becerir, Halil Rifat AlpayAbstract:This paper focuses on the assessment of the relation among constructional properties, fractional reflectances and Cover Factors of fabrics woven from polyester yarns. A novel equation for the calculation of the relation between fractional reflectance and fabric Cover Factor was proposed and the usage of the equation was assessed by reflectance measurements. 48 polyester fabrics having different constructional parameters were used and the fabrics differed from each other by their Cover Factors. The warp yarn type and count, warp density and warp yarn twist were the same but weft yarn count, weft yarn fiber count and weft density were different for the fabrics in the experimental sub-groups. The reflectance measurements were conducted on the pretreated but undyed fabric samples as well as on the individual yarn systems of the same fabrics. Fabrics with the same Cover Factors exhibited different fractional reflectances. Reflectances were found to be dependent on the Cover Factor as well as on yarn fiber fineness, yarn count, yarn density and fabric weave. The changes in crimp of the yarns according to different construction parameters also governed the changes in fractional reflectances of fabric surfaces. The proposed equation was tested according to different fabric construction parameters and it was concluded that fiber fineness and weave pattern were among the most important parameters which govern the total light reflectances from the fabric surfaces, although they are not incorporated in the calculation of the fabric Cover Factors. The proposed equation was used to explain the effects of these components on the reflectance behavior of the fabric surfaces and on fabric Cover.
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Assessment of Color Strength and Chroma Values of Polyester Fabrics having Different Cover Factors after Abrasion
Textile Research Journal, 2008Co-Authors: Mine Akgun, Behcet Becerir, Halil Rifat AlpayAbstract:This paper focuses on the assessment of color strength and chroma values that occur in dyed polyester fabrics after different cycles of abrasion for different fabric construction parameters. We used 12 polyester fabrics with various construction parameters in two experimental sets. The fabrics differed from each other by their Cover Factors. The warp yarn type and count, warp density, warp yarn twist and the fabric weave were the same for all of the fabrics in each experimental set. The dyeing of the fabric samples was performed by using a commercial disperse dye (CI Disperse Red 74:1). Four different abrasion cycles (2500, 5000, 7500, 10,000) were used. The main differences in color strength and chroma values were observed between 0 and 2500 cycles of abrasion. Increasing abrasion cycles after 2500 changed the appearance of the fabrics woven from staple weft yarns considerably. It is concluded that the rubbing motion of the Martindale instrument is related to the Cover Factor of the fabrics and dyeing co...
