Mapping and uncertainty of predictions based on multiple primary variables from joint co-simulation with Landsat TM image and polynomial regression

• Published in: Remote sensing of environment Vol. 83; no. 3; pp. 498 - 510
• Main Authors: Gertner, George, Wang, Guangxing, Fang, Shoufan, Anderson, Alan B.
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 01.12.2002; Elsevier Science
• Subjects: Applied geophysics; Areal geology. Maps; Earth sciences; Earth, ocean, space; Exact sciences and technology; Geologic maps, cartography; Internal geophysics; United States; Texas; Thematic Mapper; maps; Space remote sensing; Landsat; regression analysis; prediction; vegetation; digital simulation; cartography; soils; North America; land cover
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/S0034-4257(02)00066-4

In the management of natural resources, multiple variables correlated with each other usually need to be mapped jointly. However, joint mapping and spatial uncertainty analyses are very difficult mainly because of interactions among variables and imperfection of existing methods. There is abundant evidence that considering interactions among variables and spatial information from neighbors can result in improved maps. This study presents a remote sensing-aided method for that purpose. The method is based on the integration of joint sequential co-simulation with Landsat TM image for mapping and polynomial regression for spatial uncertainty analysis. The method was applied to a case study in which ground cover (GC), canopy cover (CC), and vegetation height (VH) were jointly mapped to derive a map of the vegetation cover factor for predicting soil loss. The variance contributions from the variables, their interactions, and the spatial information from neighbors leading to uncertainty of predicted vegetation cover factor were assessed. The results showed that in addition to unbiased maps, this method reproduced the spatial variability of the variables and the spatial correlation among them, and successfully quantified the effect of variation from all the components on the prediction of the vegetation cover factor.