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|Title:||Identifying and Quantifying Uncertainty and Spatial Disagreement in the Comparison of Global Land Cover for Different Applications|
|Authors:||FRITZ Steffen; SEE Linda|
|Citation:||GLOBAL CHANGE BIOLOGY vol. 14 p. 1057-1075|
|Type:||Articles in periodicals and books|
|Abstract:||This paper provides a methodology for comparing global land cover maps that allows for differences in legend definitions between products to be taken into account. The legends of the two maps are first reconciled by creating a legend lookup table that shows how the legends map onto one another. Where there is overlap, the specific definitions for each legend class are used to calculate the degree of overlap between legend classes. In this way, one-to-many mappings are accounted for unlike in most methods where the legend definitions are often forced into place. Application specific requirements are captured using expert input, whereby the user rates the importance of disagreement between different legend classes based on the needs of the application. This user-defined matrix in conjunction with the degree of overlap between legend classes is applied on a pixel-by-pixel basis to create maps of spatial disagreement and uncertainty. The user can then highlight the areas of highest thematic uncertainty and disagreement between the different land cover maps allowing for areas that require further detailed examination to be readily identified. Alternatively, the user could combine the products into a hybrid land cover map that better serves their needs. The methodology is illustrated using different land cover products including Global Land Cover 2000 and the MODIS land cover data set. Two diverse applications are provided including the estimation of global forest cover and monitoring of agricultural land. In the case of global forest cover, an example was provided for Columbia which showed that the MODIS land cover map overestimates forest cover in comparison to the GLC-2000. The agricultural example, on the hand, served to illustrate that for Sudan, MODIS underestimates crop areas while GLC-2000 overestimates them so the decision as to which land cover product to use is much more complicated. One of the main advantages of this approach is the ability to involve the user.|
|JRC Institute:||Space, Security and Migration|
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