The Experts below are selected from a list of 4707 Experts worldwide ranked by ideXlab platform
Xuehua Han - One of the best experts on this subject based on the ideXlab platform.
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using monte carlo simulation to improve the performance of Semivariograms for choosing the remote sensing imagery resolution for natural resource surveys case study on three counties in east central and west china
ISPRS international journal of geo-information, 2018Co-Authors: Juanle Wang, Junxiang Zhu, Xuehua HanAbstract:Semivariograms have been widely used in research to obtain optimal resolutions for ground features. To obtain the Semivariogram curve and its attributes (range and sill), parameters including sample size (SS), maximum distance (MD), and group number (GN) have to be defined, as well as a mathematic model for fitting the curve. However, a clear guide on parameter setting and model selection is currently not available. In this study, a Monte Carlo simulation-based approach (MCS) is proposed to enhance the performance of Semivariograms by optimizing the parameters, and case studies in three regions are conducted to determine the optimal resolution for natural resource surveys. Those parameters are optimized one by one through several rounds of MCS. The result shows that exponential model is better than sphere model; sample size has a positive relationship with R2, while the group number has a negative one; increasing the simulation number could improve the accuracy of estimation; and eventually the optimized parameters improved the performance of Semivariogram. In case study, the average sizes for three general ground features (grassland, farmland, and forest) of three counties (Ansai, Changdu, and Taihe) in different geophysical locations of China were acquired and compared, and imagery with an appropriate resolution is recommended. The results show that the ground feature sizes acquired by means of MCS and optimized parameters in this study match well with real land cover patterns.
Alan B Anderson - One of the best experts on this subject based on the ideXlab platform.
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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.
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determination of frequency for remeasuring ground and vegetation cover factor needed for soil erosion modeling
Environmental Management, 2006Co-Authors: George Z Gertner, Guangxing Wang, Alan B AndersonAbstract:Determining a remeasurement frequency of variables over time is required in monitoring environmental systems. This article demonstrates methods based on regression modeling and spatio-temporal variability to determine the time interval to remeasure the ground and vegetation cover factor on permanent plots for monitoring a soil erosion system. The spatio-temporal variability methods include use of historical data to predict Semivariograms, modeling average temporal variability, and temporal interpolation by two-step kriging. The results show that for the cover factor, the relative errors of the prediction increase with an increased length of time interval between remeasurements when using the regression and Semivariogram models. Given precision or accuracy requirements, appropriate time intervals can be determined. However, the remeasurement frequency also varies depending on the prediction interval time. As an alternative method, the range parameter of a Semivariogram model can be used to quantify average temporal variability that approximates the maximum time interval between remeasurements. This method is simpler than regression and Semivariogram modeling, but it requires a long-term dataset based on permanent plots. In addition, the temporal interpolation by two-step kriging is also used to determine the time interval. This method is applicable when remeasurements in time are not sufficient. If spatial and temporal remeasurements are sufficient, it can be expanded and applied to design spatial and temporal sampling simultaneously.
George Z Gertner - One of the best experts on this subject based on the ideXlab platform.
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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.
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determination of frequency for remeasuring ground and vegetation cover factor needed for soil erosion modeling
Environmental Management, 2006Co-Authors: George Z Gertner, Guangxing Wang, Alan B AndersonAbstract:Determining a remeasurement frequency of variables over time is required in monitoring environmental systems. This article demonstrates methods based on regression modeling and spatio-temporal variability to determine the time interval to remeasure the ground and vegetation cover factor on permanent plots for monitoring a soil erosion system. The spatio-temporal variability methods include use of historical data to predict Semivariograms, modeling average temporal variability, and temporal interpolation by two-step kriging. The results show that for the cover factor, the relative errors of the prediction increase with an increased length of time interval between remeasurements when using the regression and Semivariogram models. Given precision or accuracy requirements, appropriate time intervals can be determined. However, the remeasurement frequency also varies depending on the prediction interval time. As an alternative method, the range parameter of a Semivariogram model can be used to quantify average temporal variability that approximates the maximum time interval between remeasurements. This method is simpler than regression and Semivariogram modeling, but it requires a long-term dataset based on permanent plots. In addition, the temporal interpolation by two-step kriging is also used to determine the time interval. This method is applicable when remeasurements in time are not sufficient. If spatial and temporal remeasurements are sufficient, it can be expanded and applied to design spatial and temporal sampling simultaneously.
Karl W Birkeland - One of the best experts on this subject based on the ideXlab platform.
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surface hoar distribution at the scale of a helicopter skiing operation
Proceedings 2012 International Snow Science Workshop Anchorage Alaska, 2012Co-Authors: Matthew Borish, Stephan G. Custer, Stuart Challender, Karl W Birkeland, Jordy HendrikxAbstract:Understanding what controls coarse scale snowpack properties, such as surface hoar distribution, is imperative for predicting snow avalanches. Due in part to the inherent difficulties of winter travel in mountainous terrain, most spatial variability investigations of snow properties have been limited to relatively fine scales. To quantify snow surface spatial variability at the basin, region, and mountain range scales, a team of heli-skiing guides collected data throughout four major surface hoar formation periods over two heli-skiing seasons in rugged alpine terrain near Haines, Alaska across an extent of nearly 60km. Geographically weighted regression revealed a positive relationship between elevation and s urface hoar crystal size with adjusted R 2 values averaging near 0.40. Geostatistical analysis yielded spherical Semivariogram autocorrelation ranges from approximately 3-25km, which is similar in size to many of the basins and regions within the study area. Kriging models built from the Semivariograms were produced to aid geographic visualization of coarse scale snowpack processes. The results of this research suggest it may be possible to identify areas with greater surface hoar growth and persistence potentials as a consequence of synoptic onshore or offshore flow, and glacially influenced katabatic winds. These results can help in future efforts to forecast snow stability patterns over large areas.
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reliability of sampling designs for spatial snow surveys
Computers & Geosciences, 2007Co-Authors: Kalle Kronholm, Karl W BirkelandAbstract:Spatial patterns are an inherent property of most naturally occurring materials at a large range of scales. To describe spatial patterns in the field, several observations are made according to a certain sampling design. The spatial structure can be described by the Semivariogram range, and nugget and sill variances. We test how reliably seven sampling designs estimate these parameters for simulated spatial fields with predefined spatial structures using a Monte Carlo approach. Five designs have been used previously in the field for snow cover sampling, whereas two designs with semi-random sampling locations have not been used in the field. The designs include 84-159 sampling locations covering small mountain slopes typical of snow avalanche terrain. The results from the simulations show that all designs: (a) give reasonably unbiased estimates of the Semivariogram parameters when averaged over many simulations, and (b) show considerable spread in the Semivariogram parameter estimates, causing large uncertainty in the Semivariogram estimates. Our results suggest that any comparisons of the estimated Semivariogram parameters made with the sampling designs will be associated with large uncertainties. To remedy this, we suggest that optimal sampling designs for sampling slope scale snow cover parameters must include more sampling locations and a stratified randomized sampling design in the future.
Juanle Wang - One of the best experts on this subject based on the ideXlab platform.
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using monte carlo simulation to improve the performance of Semivariograms for choosing the remote sensing imagery resolution for natural resource surveys case study on three counties in east central and west china
ISPRS international journal of geo-information, 2018Co-Authors: Juanle Wang, Junxiang Zhu, Xuehua HanAbstract:Semivariograms have been widely used in research to obtain optimal resolutions for ground features. To obtain the Semivariogram curve and its attributes (range and sill), parameters including sample size (SS), maximum distance (MD), and group number (GN) have to be defined, as well as a mathematic model for fitting the curve. However, a clear guide on parameter setting and model selection is currently not available. In this study, a Monte Carlo simulation-based approach (MCS) is proposed to enhance the performance of Semivariograms by optimizing the parameters, and case studies in three regions are conducted to determine the optimal resolution for natural resource surveys. Those parameters are optimized one by one through several rounds of MCS. The result shows that exponential model is better than sphere model; sample size has a positive relationship with R2, while the group number has a negative one; increasing the simulation number could improve the accuracy of estimation; and eventually the optimized parameters improved the performance of Semivariogram. In case study, the average sizes for three general ground features (grassland, farmland, and forest) of three counties (Ansai, Changdu, and Taihe) in different geophysical locations of China were acquired and compared, and imagery with an appropriate resolution is recommended. The results show that the ground feature sizes acquired by means of MCS and optimized parameters in this study match well with real land cover patterns.
