Efficiencies of remotely sensed data and sensitivity of grid spacing in sampling and mapping a soil erosion relevant cover factor by cokriging

Abstract

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 variables with the cover factor can provide the basis for an increase in accuracy. Secondly, this procedure can be applied to investigate the appropriate spatial resolution of imagery, which, for sampling and mapping the cover factor, should be 90 m × 90 m – nearly consistent with the line transect size of 100 m used for the ground field survey. In addition, we recommend using the average of cokriging variance to determine the global grid spacing of samples, instead of the maximum cokriging variance.