Spatial-variability-based algorithms for scaling-up spatial data and uncertainties

• Published in: IEEE transactions on geoscience and remote sensing Vol. 42; no. 9; pp. 2004 - 2015
• Main Authors: Guangxing Wang, Gertner, G.Z., Anderson, A.B.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.09.2004; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Agricultural engineering; Algorithms; Applied geophysics; Councils; Earth sciences; Earth, ocean, space; Environmental management; Estimates; Exact sciences and technology; Ground support; Internal geophysics; Management information systems; Mathematical analysis; Mathematical models; Remote sensing; Resource management; Spatial resolution; Uncertainty; Vegetation mapping; geostatistics; algorithms; Thematic Mapper; natural resources; stratification; simulation; accuracy; remote sensing; vegetation; cartography; uncertainty; trees; spatial distribution; vegetation cover mapping; scaling-up; spatial variability; imagery; spatial resolution; information systems; remotely sensed data; errors
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2004.831889

When using remote sensing and geographic information systems, accurately scaling- up spatial data of a variable and their uncertainties from a finer to a coarser spatial resolution is widely required in mapping and managing natural resources and ecological and environmental systems. In this study, four up-scaling methods were derived based on simple and ordinary cokriging estimators and a sequential Gaussian cosimulation algorithm for points and blocks. Taking spatial variability of variables into account in the up-scaling process made it possible to simultaneously and accurately obtain estimates and estimation variances of larger blocks from sample and image data of smaller supports. With the aid of Thematic Mapper imagery, these methods were compared in a case study where overall vegetation and tree covers were scaled up from a spatial resolution of 30/spl times/30 m/sup 2/ to 90/spl times/90 m/sup 2/ with a stratification method at 90/spl times/90 m/sup 2/. The results showed that the methods Point simple coKriging/spl I.bar/Point co-Simulation scaling UP (PsK/spl I.bar/PSUP) and PsK/spl I.bar/Block co-Simulation (PsK/spl I.bar/BS) led to smaller errors and better reproduced spatial distribution and variability of the variables than the other methods. Choosing PsK/spl I.bar/PSUP or PsK/spl I.bar/BS depends on the users' emphasis on accuracy of estimates and variances, computational time, etc. The methods can be applied to multiple continuous variables that have any distribution. It is also expected that the general idea behind the methods can be expanded to scaling-up spatial data for categorical variables.