Local variability based sampling for mapping a soil erosion cover factor by co-simulation with Landsat TM images

• Published in: International journal of remote sensing Vol. 27; no. 12; pp. 2423 - 2447
• Main Authors: Anderson, A. B., Wang, G., Gertner, G.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 01.06.2006; Taylor and Francis
• Subjects: Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Internal geophysics; Teledetection and vegetation maps; Variable; data; natural resources; kriging; simulation; global; Image; Sampling design; collecting; Variance; ecology; Sequential; Distance; Cost efficiency analysis; maps; Space remote sensing; Landsat; Spacing; cartography; Method; Landsat satellite; Optimum; Optimal sampling; Environment; soil erosion
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/01431160600554413

When using remotely sensed data, a cost-efficient sampling design for collecting ground data is needed to accurately map natural resources, environmental and ecological systems. The existing methods including traditional simple random sampling and kriging or cokriging variance based sampling designs can not lead to optimal sampling designs. In this study, a local variability based sampling design using a sequential Gaussian co-simulation by combining remotely sensed and ground data is developed. This method theoretically can lead to a sampling design with variable sampling distances, that is, grid spacings that are optimal at local and global levels. The method was assessed and compared with simple random sampling in a case study in which the soil erosion ground and vegetation cover factor was sampled and mapped using Landsat Thematic Mapper (TM) images and annual permanent ground measurements sampled from 1989 to 1995. The results show that the local variability based sampling greatly reduced the number of sampled plots and increased the cost-efficiency for sampling in comparison to simple random sampling. The difference in cost-efficiency between the two methods increased with increased global variation. This method can also be applied to analyse the sufficiency of a permanent plot sample and further provide information for additional sampling.