Prototyping a probability-based Best Map Approach for global land cover 
datasets at 1km resolution using MODIS, GLC2000, UMD and IGBP

Göhmann, H.; Herold, M.; Jung, Martin; Schulz, M.; Schmullius, C.

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Prototyping a probability-based Best Map Approach for global land cover datasets at 1km resolution using MODIS, GLC2000, UMD and IGBP

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Jung, Martin
Global Diagnostic Modelling, Dr. Martin Jung, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Citation

Göhmann, H., Herold, M., Jung, M., Schulz, M., & Schmullius, C. (2009). Prototyping a probability-based Best Map Approach for global land cover datasets at 1km resolution using MODIS, GLC2000, UMD and IGBP. In Sustaining the millennium development goals: proceedings; 33rd International Symposium on Remote Sensing of Environment, ISRSE-33 (pp. 404-407). Stresa: International Symposium on Remote Sensing of Environment.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0029-2333-4

Abstract

Different approaches have been developed and used for global land cover mapping. Differences, heterogeneities and problems are largely understood and the logical next step is to combine a suite of datasets into a "best" map based on accuracy data. The prototype activity here uses global probabilities derived in a comparative accuracy assessment in combination with the spatial homogeneity to generate a synthetic new dataset of higher quality. A pixel-probability was derived from a comparative validation of the four input datasets and used to derive a global best map prototype with coarse thematic detail. We present the approach using 5 generic classes.