TY - GEN
T1 - Sampling designs over time based on spatial variability of images for mapping and monitoring soil erosion cover factor
AU - Wang, Guangxing
AU - Anderson, Alan B.
AU - Gertner, George
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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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M3 - Conference contribution
AN - SCOPUS:84869006797
SN - 9781604237290
T3 - American Society for Photogrammetry and Remote Sensing - Annual Conference of the American Society for Photogrammetry and Remote Sensing 2006: Prospecting for Geospatial Information Integration
SP - 1514
EP - 1527
BT - American Society for Photogrammetry and Remote Sensing - Annual Conference of the American Society for Photogrammetry and Remote Sensing 2006
T2 - Annual Conference of the American Society for Photogrammetry and Remote Sensing 2006: Prospecting for Geospatial Information Integration, ASPRS 2006
Y2 - 1 May 2006 through 5 May 2006
ER -