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

Guangxing Wang, George Z. Gertner, Alan B. Anderson

Research output: Contribution to journalArticlepeer-review


When using remote sensing and geogrphaic 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 × 30 m2 to 90 × 90 m2 with a stratification method at 90 × 90 m2. The results showed that the methods Point simple coKriging_Point co-Simulation scaling UP (PsK_PSUP) and PsK_Block co-Sim-ulation (PsK_BS) led to smaller errors and better reproduced spatial distribution and variability of the variables than the other methods. Choosing PsK_PSUP or PsK_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.

Original languageEnglish (US)
Pages (from-to)2004-2015
Number of pages12
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number9
StatePublished - Sep 2004


  • Geostatistics
  • Remotely sensed data
  • Scaling-up
  • Spatial variability
  • Uncertainty
  • Vegetation cover mapping

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)


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