Land cover classification on a global scale is not an easy task. Currently urban areas are represented worldwide by only one single category, which essentially says that all the urban lands around the world look the same. Overall the planet’s urban lands are also under-classified, since given most classification schemes built-up land is usually the area left over after all other categories have been classified.
Initially the two source data sets were re-gridded to five-minute resolution. In the newly produced nighttime lights data, each cell represented the average nighttime light value of four cells, while each cell in the new land cover data represented the number of cells out of 100 which were categorized in the original IGBP data as urban or built-up land.
A linear regression was developed for all non-zero values to establish the relation between the built-up area density, as the dependent variable, and nighttime lights, as the independent variable. The statistics were then applied to the nighttime data set to develop modeled built-up area density. To this data set, we added the positive difference to the fractional re-gridded IGBP data set.
Thus, the resultant data contains a combination of modeled built-up areas (base on nighttime lights) and observed built-up areas (based on IGBP land cover data).
Billie Miteva (SAGE, firstname.lastname@example.org), Personal Communication. Please email Billie for a more complete citation.
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